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A new high-response self-balancing sensorless control system of induction motor for weft accumulator
Mechatronics 62 (2019) 102249
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A new high-response self-balancing sensorless control system of induction motor for weft accumulator ✩ Wenqi Lu a,1,∗, Di Wu a, Yansuo Zhou b, Kaiyuan Lu c,∗, Dong Wang c, Hao Wu a, Liangliang Yang a a
Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China Faculty of Mechanical Engineering, Tianjin University, Tianjin 300072, China c Department of Energy Technology, Aalborg University, Aalborg DK-9220, Denmark b
a r t i c l e
i n f o
Keywords: Weft accumulator High-response Self-balancing Sensorless Model reference adaptive control Optical electromechanically integrated
a b s t r a c t The high-response and self-adaptive yarn delivery system used in weft accumulator is a critical component for improving the efficiency and quality of textile machinery. However, the existing drive system in the weft accumulator has problems such as slow start-up and slow speed-tracking responses, giving difficulties in achieving a high-efficient yarn delivery of the textile machinery. In this paper, a high-response self-balancing sensorless integrated control system of the induction motor for weft accumulator is proposed. Firstly, to enable the motor to start and respond adaptively and quickly to the number of yarn control loops and the speed control of the external equipment, a high-response sensorless vector control system for induction motor based on improved model reference adaptive control(MRAC) is proposed. Secondly, to achieve a high-response adaptive tracking performance of yarn input and output numbers and spool shaft speed, a self-balancing control strategy based on optical electromechanically integrated closed-loop control is proposed. For proving the effectiveness of the proposed scheme, a corresponding test platform was constructed for testing and validation. It can be observed from the results that the system designed in this paper has fast starting speed and better speed tracking performance, satisfying yarn delivery requirements at different speeds with high-response and adaptability.
1. Introduction Weft accumulator is a temporary storage device of the unwinding weft yarn on the cylinder before it is introduced into the shuttle. It is used in the rapier loom, shuttle loom, water jet loom and air jet loom, etc. The temporary weft yarns are arranged neatly and wound on the smooth cylinder or cone surface, which creates a good condition for weft insertion at high speed. It also allows the unwinding yarn to obtain more uniform tension, thus avoiding large tension fluctuation during the unwinding of yarn and reducing yarn failure consequently. It has become the critical equipment for many textile production lines, and its performance directly affects the efficiency and quality of the entire production line. In such a system, the variable frequency motor drive performance and the fast detection and tracking ability of yarn input and output signals are two core technologies of the weft accumulator, which directly determine whether the weft accumulator can meet the requirements with respect to yarn delivery tension and production speed of different textile machinery [1–3]. It has become a hot topic to study in this field.
✩ ∗
1
Currently, there are three kinds of drive systems for variable frequency motor drives: induction motor drives, permanent magnet synchronous motor drives, and brushless DC motor drives. References [4,5] adopted a permanent magnet synchronous motor with a vector control drive system. The designed motor control system has demonstrated good speed response, and can effectively meet the control requirements for weft accumulator motor. But the cost of the permanent magnet synchronous motor drive system is very high. It is more suitable for cost-insensitive applications, not for cost-efficient applications like for the weft accumulator. References [6,7] adopted a brushless DC motor sensorless control system. Torque ripple caused by current commutation and slot effect based on vector control of the Brushless DC motor was studied in these papers. A speed control scheme using improved space vector pulse width modulation (SVPWM) and possible cogging torque ripple compensation method were proposed. But in practical operations, the motor of the weft accumulator needs a quick start and stop function. The brushless DC motor sensorless drive system has poor controllability in the initial start-up process and has difficulty in satisfying the fast start-up requirement for the weft accumulator. Compared with the
This paper was recommended for publication by Associate Editor Dr. Marcel Francois Heertjes. Corresponding authors. E-mail addresses: [email protected] (W. Lu), [email protected] (K. Lu). 23#327, Zhejiang Sci-Tech University, 928 Second Avenue, Xiasha Higher Education Zone, Hangzhou, Zhejiang, PR China.
https://doi.org/10.1016/j.mechatronics.2019.07.001 Received 10 September 2018; Received in revised form 25 June 2019; Accepted 5 July 2019 0957-4158/© 2019 Elsevier Ltd. All rights reserved.
W. Lu, D. Wu and Y. Zhou et al.
two kinds of motor drive systems mentioned above, the induction motor drive system has good controllability in the initial start-up process, and its cost is low. The induction motor drive system is considered as a more suitable candidate for the weft accumulator. Many control strategies have been proposed for induction motor drive systems. References [8–10] presented a simple control strategy with a constant voltage to frequency ratio for a three-phase induction motor. It has advantages of simple hardware implementation, good stability and steady-state performance. But this control strategy is an open-loop control method, which has problems of low speed regulation accuracy and poor dynamic performance. References [11,12] presented a slip frequency control scheme derived from the rotating stator magnetic field oriented induction machine model. The slip frequency is minimal when the motor is at a steady state. The torque of the motor can then be approximated to be proportional to the slip angular frequency while maintaining a constant rotor flux linkage. Therefore, the torque of the motor can be controlled by simply controlling the slip angular frequency, and the speed can be controlled and adjusted consequently. However, the most significant disadvantage of this control method is that it is only suitable for situations where the speed is constant or the speed changes slowly. References [13,14] presented a direct torque control method, which chooses the appropriate voltage space vector directly according to the position of the stator flux, the deviation of stator flux amplitude and the deviation of the electromagnetic torque to realize a direct control of the torque. It exhibits good starting torque and fast dynamic response. However, this control scheme results in large torque ripple and large steady-state error. It is suitable for small speed range and low steady-state accuracy applications. Moreover, these control methods usually use motor shaft sensors to measure real motor speed. The cost of such a drive system with a sensor is high, and the application is restricted since how to mechanically integrate the shaft sensor to the motor is a challenge for many compact drive system applications. If the speed sensorless control strategy can be adopted, this problem can be avoided. In recent years, a large number of literatures have studied speed sensorless control strategies for induction motors. References [15,16] presented a speed identification method based on stator and rotor flux linkage and angular slip speed. It is simple and easy to implement, but the motor flux linkage and other parameters of the motor are needed. The speed estimation accuracy depends on the motor parameters which could change in different working conditions. Besides, the speed identification process is open-loop control, so there is no error correction measure. References [17–19] presented a speed identification method based on a full-order state observer. It does not depend on the induction motor parameters, but this method is complex and challenging to implement in practical DSP-based applications. References [20–23] presented a method using an extended Kalman filter. The advantage of this method is its ability to reduce the influence of measurement noise and random interference signals effectively. But this method is based on the known errors and measured noise statistics. For different motors, the corresponding measured noise statistics are needed to calibrate the motor characteristic parameters so that the practicability and adaptability of this method are poor. References [24–28] presented a method based on a model reference adaptive system. According to the designed adaptive law, the motor parameters are identified by forcing the error of the reference model and the output of the adjustable model to zero. It has advantages of high robustness and simple realization. But the reference model has problems of slow convergence speed, limited variable speed range, integral drift and initial value due to the influence of stator resistive voltage drop and the use of a pure integrator. As a result, the starting response of the motor is slow, which cannot meet the high efficient yarn delivery requirement for weft accumulator. For fast detection and tracking of yarn input and output signals, most of the existing weft accumulator drive systems adopt the compensation method based on an open-loop control, and the detection speed of yarn input and output signals is slow. It has difficulty in realizing a high adaptive tracking performance of yarn input and output signals [1–3].
Mechatronics 62 (2019) 102249
Therefore, firstly, to improve the starting speed and speed-tracking performance of the motor, a high-response, vector control system based on the improved model reference adaptive control (MRAC) algorithm for the induction motor without position sensor is proposed. Secondly, for improving the speed-tracking performance of the yarn input and output, a self-balancing control strategy is proposed based on an optical electromechanically integrated closed-loop control system. Finally, by integrating the above two methods, a high-response and self-balancing sensorless induction motor control system for weft accumulator is proposed. To verify the effectiveness of the design method and the developed system, besides the theoretical analysis, prototype design and experimental tests are carried out. In this paper, in section II, the design principle of a high-response self-balancing sensorless integrated control system of the induction motor for weft accumulator is presented. The proposed new method includes two solutions. First, a high-response sensorless vector control strategy based on an improved model reference adaptive algorithm is discussed in section II.1. Secondly, a self-balancing control strategy of yarn input and output based on an optical electromechanically integrated closed-loop control is given in section II.2. In section III, the experimental validation is carried out. Section IV concludes this paper. 2. The proposed new high-response and self-balancing sensorless integrated control system The structural block diagram of the weft accumulator studied in this paper is shown in Fig. 1, where optoelectronics and electromechanics are highly integrated. It consists of a yarn input port, a roof, an induction motor, a speed regulating wheel, a controller, a yarn input detection circuit, a yarn output detection circuit, a yarn winding mechanism, a yarn output port and other supporting parts. The working principle of the weft accumulator is described as follows: the yarn output detection circuit measures the yarn output number and its sending speed; the obtained speed is taken as the reference speed for the controller. The induction motor will then drive the speed regulating wheel and the winding shaft, pulling the yarn from the input port to the winding shaft. At the same time, the yarn input detection circuit measures the yarn input number and speed, and the self-balancing control of the yarn input and output number and speed needs to be implemented. In such a system, the variable frequency motor drive performance (fast start-up and speed regulation ability) and fast detection and tracking capability of yarn input and output signals are the two core technologies of this control system. For improving the speed tracking ability of yarn input and output and reduce the motor losses, a new high dynamic response and selfbalancing sensorless integrated control system are proposed in this paper, as shown in Fig. 2. It includes two solutions. Firstly, a high-response sensorless vector control strategy based on the improved model reference adaptive algorithm is proposed for the induction motor drive system. Secondly, a self-balancing control strategy of yarn input and output based on an optical electromechanically integrated closed-loop control is proposed. The two solutions are discussed in detail below.
Fig. 1. The structural block diagram of weft accumulator.
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Mechatronics 62 (2019) 102249
Fig. 2. The principal block diagram of a new high-response and self-balancing sensorless integrated control system.
𝑢𝑠𝛽 = 𝑅𝑠 𝑖𝑠𝛽 +
𝑑 𝜓𝑠𝛽
𝑢𝑟𝛼 = 0 = 𝑅𝑟 𝑖𝑟𝛼 + 𝑢𝑟𝛽 = 0 = 𝑅𝑟 𝑖𝑟𝛽 +
Fig. 3. The basic composition structure of traditional MRAC.
2.1. High-response sensorless vector control strategy of induction motor based on an improved MRAC The traditional MRAC method [24–28] for sensorless speed identification of induction motors usually consists of three parts: a reference model, an adjustable model and an adaptive algorithm. Its principle block diagram is shown in Fig. 3. The reference model is generally used to calculate the actual rotor flux and its angle, which is obtained by a voltage model using a pure integrator. The adjustable model is used to estimate the rotor flux, which utilizes a current model. The adaptive algorithm is used to calculate the rotor speed, by using a PI regulator with fixed coefficients. The voltage model has problems of slow convergence speed, limited variable speed range, integral drift and initial value due to the influence of stator resistive voltage drop and the use of a pure integrator [24–28]. An improved rotor flux observer based on a first-order inertial filter with rotor flux compensation is adopted in this paper, to overcome the integral drift and initial value problems [29,30]. The problems of slow convergence speed and limited speed range are alleviated by using a PI regulator with adaptive coefficients. The structural block diagram of the improved MRAC is shown in Fig. 4. The following is a description of its design principles. (1) The rotor flux and angle estimator based on an improved voltage model In the stationary reference frame, the stator voltage equation, rotor voltage equation, flux linkage equation and the electromagnetic torque equation of an induction motor can be obtained as follows: 𝑢𝑠𝛼 = 𝑅𝑠 𝑖𝑠𝛼 +
𝑑 𝜓𝑠𝛼 𝑑𝑡
(1)
(2)
𝑑𝑡 𝑑 𝜓𝑟𝛼 + 𝜔𝑟 𝜓𝑟𝛽 𝑑𝑡 𝑑 𝜓𝑟𝛽 𝑑𝑡
(3)
− 𝜔𝑟 𝜓𝑟𝛼
(4)
𝜓𝑠𝛼 = 𝐿𝑠 𝑖𝑠𝛼 + 𝐿𝑚 𝑖𝑟𝛼
(5)
𝜓𝑠𝛽 = 𝐿𝑠 𝑖𝑠𝛽 + 𝐿𝑚 𝑖𝑟𝛽
(6)
𝜓𝑟𝛼 = 𝐿𝑟 𝑖𝑟𝛼 + 𝐿𝑚 𝑖𝑠𝛼
(7)
𝜓𝑟𝛽 = 𝐿𝑟 𝑖𝑟𝛽 + 𝐿𝑚 𝑖𝑠𝛽
(8)
𝑇𝑒 =
3 𝐿𝑚 𝑝 (𝜓 𝑖 − 𝜓𝑠𝛽 𝑖𝑠𝛼 ) 2 𝑝 𝐿𝑟 𝑠𝛼 𝑠𝛽
(9)
In a rotating d-q reference frame, the current model of the motor rotor flux can be obtained as follows: 𝐿𝑚 𝑖 − 𝑇𝑟 𝑠𝑑 𝐿𝑚 = 𝑖 − 𝑇𝑟 𝑠𝑞
𝑝𝜓𝑟𝑑 = 𝑝𝜓𝑟𝑞
1 𝜓 − j(𝜔𝑠 − 𝜔𝑟 )𝜓𝑟𝑞 𝑇𝑟 𝑟𝑑 1 𝜓 − j(𝜔𝑠 − 𝜔𝑟 )𝜓𝑟𝑑 𝑇𝑟 𝑟𝑞
(10)
The rotor back EMF can be obtained as follows: 𝐿𝑟 (𝑢 − 𝑅𝑠 𝑖𝑠𝛼 − 𝜎𝐿𝑠 𝑝𝑖𝑠𝛼 ) 𝐿𝑚 𝑠𝛼 𝐿𝑟 = (𝑢 − 𝑅𝑠 𝑖𝑠𝛽 − 𝜎𝐿𝑠 𝑝𝑖𝑠𝛽 ) 𝐿𝑚 𝑠𝛽
𝑒𝑟𝛼 = 𝑝𝜓𝑟𝛼 = 𝑒𝑟𝛽 = 𝑝𝜓𝑟𝛽
(11)
where, us𝛼 , us𝛽 is the stator winding resistance and rotor winding resistance respectively, us𝛼 ,us𝛽 ,ur𝛼 ,ur𝛽 is the 𝛼 and 𝛽axis components of the stator and rotor voltage respectively, is𝛼 ,is𝛽 , ir𝛼 , ir𝛽 is the 𝛼and𝛽axis components of the stator and rotor current respectively, Ls ,Lr ,Lm is the stator inductance, rotor inductance and mutual inductance between stator and rotor respectively, 𝜓 s𝛼 ,𝜓 s𝛽 ,𝜓 r𝛼 ,𝜓 r𝛽 is the 𝛼and𝛽axis component of stator and rotor flux respectively,𝜓 rd ,𝜓 rd is the d and q axis component of rotor flux respectively,Te is the electromagnetic torque, pp is the number 𝜏 of pole-pairs, 𝜔r is the rotor actual velocity, 𝜓𝑠𝛽 (𝑘) = 𝜏 +1𝑇 [𝜓𝑠𝛽 (𝑘 − 1) + 1
𝑠
𝑇𝑠 (𝑢𝑠𝛽 (𝑘) − 𝑅𝑠 𝑖𝑠𝛽 (𝑘))] is the magnetic flux leakage coefficient,er𝛼 ,er𝛽 are the rotor back EMF,p is the differentiation operator, Tr is the rotor time constant,𝜔s is the rotating speed of the stator magnetic field.
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Mechatronics 62 (2019) 102249
Fig. 4. The basic composition structure of improved MRAC.
Fig. 5. The rotor flux estimation based on improved voltage model method.
Fig. 6. The principle block diagram of the high-response sensorless vector control system of induction motor based on an improved MRAC algorithm.
Fig. 7. The schematic diagram of yarn input and output detection system based on optical electromechanically integrated.
In order to solve the integral drift and initial value problems, an improved rotor flux observer using a first-order inertial filter is adopted [29,30]; for reducing the amplitude and phase error of the estimated flux and improving the dynamic response of the motor, the rotor flux current model in the rotor flux oriented reference frame is used to compensate the error. The first-order inertial filter is an effective combination of a
Fig. 8. The schematic diagram of yarn input and output number and speed detection circuit based on optoelectronic devices.
pure integrator and a first-order high-pass filter. The complete schematic block diagram of the rotor flux estimator is shown in Fig. 5.
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Mechatronics 62 (2019) 102249
Fig. 9. The principal block diagram of the closed-loop control strategy based on an integral separation PI regulator.
When the rotor speed is low (smaller than 200r/min) , to improve the dynamic response of the system and reduce the convergence time, the value of kpm , kim is chosen to be large values, and their values are fixed (𝑘𝑝m =𝐾𝑝 ,𝑘im = 𝐾𝑖 ). When the rotor speed increases and is higher than 200r/min, to reduce the steady-state error and reduce the overshoot and settling time, the coefficients kpm , kim are chosen to be rotor speed dependent values as described in (14) and (15). { 𝐾𝑝 𝜔cmd < 200 𝑘𝑝m = (14) 𝐾𝑝 𝜔200 𝜔cmd ≥ 200 cmd { 𝐾𝑖 𝜔cmd < 200 𝑘im = (15) 𝐾𝑖 − 𝐾0 (𝜔cmd − 200) 𝜔cmd ≥ 200 where, Kp and Ki are constant values; K0 is the slope coefficient, and 𝜔cmd is the given rotor speed. Finally, the rotor estimated speed 𝜔̂ 𝑟 can be calculated by: 𝜔̂ 𝑟 =
𝑡
∫0
𝑘im (𝜓̂ 𝑟𝛽 𝜓𝑟𝛼1 − 𝜓̂ 𝑟𝛼 𝜓𝑟𝛽1 )𝑑𝑡 + 𝑘𝑝m (𝜓̂ 𝑟𝛽 𝜓𝑟𝛼1 − 𝜓̂ 𝑟𝛼 𝜓𝑟𝛽1 )
(16)
(3) A sensorless vector control system based on the improved MRAC
Fig. 10. The integral separation PI regulator.
Finally, the rotor flux may be estimated by: 𝜓𝑟1 =
𝑇𝑐 𝑝𝜓𝑟 +𝜓𝑟∗ 𝑇𝑐 1 1 𝑒𝑟 + 𝜓𝑟∗ = = 𝜓𝑟 +(𝜓𝑟∗ − 𝜓𝑟 ) 1 + 𝑇𝑐 𝑝 1 + 𝑇𝑐 𝑝 1 + 𝑇𝑐 𝑝 1 + 𝑇𝑐 𝑝
(12)
where, Tc is the cutoff frequency of the filter. According to (12), the actual flux of the rotor is equal to the reference flux, when the system is in steady state, which means 𝜓𝑟 = 𝜓𝑟∗ and the error is zero. 𝜓𝑟1 = 𝜓𝑟 = 𝜓𝑟∗ is also valid, which means the improved voltage model can realize zero error of flux magnitude and phase estimations in steady-state operation. When the system is running in dynamic conditions, the initial flux𝜓𝑟 ≠ 𝜓𝑟∗ , but 𝜓 r will converge to 𝜓𝑟∗ asymptotically. Its convergence speed is related to the time constant Tc . According to (12), the rotor flux angle can be calculated as follows. ( 𝜃𝜓𝑟 = arctan
𝜓𝑟𝛽1 𝜓𝑟𝛼1
) (13)
(2) The rotor speed estimator based on an adaptive coefficient PI regulator This paper adopts an improved voltage model to solve the integral drift and initial value problems caused by a pure integrator. However, due to the influence of stator resistance, the convergence speed of the reference model is slow and large speed estimation error when running at low speed may occur. An adaptative parameter PI regulator is then proposed to solve this problem. The proportional gain kpm and integrator coefficient kim of the PI regulator is adjusted according to (14) and (15).
Based on the above analysis, a high-response sensorless vector control system for induction motor based on the improved MRAC algorithm is proposed in this paper, as shown in Fig. 6. It consists of a speed control loop, a current control loop, the improved MRAC, a SVPWM, a threephase inverter, and an induction motor, etc. In this system, the outer speed control loop uses the estimated speed obtained by the observer based on the improved MRAC for speed regulation. The speed error is fed into a speed regulator, which generates the quadrature axis current command 𝑖𝑠𝑞 _𝑟𝑒𝑓 for the inner current control loop. The quadrature axis current command 𝑖𝑠𝑞 _𝑟𝑒𝑓 and its actual feedback value isq are inputs into the quadrature axis current regulator, and then the q-axis voltage command usq is generated. The d-axis voltage command usd is generated similarly. Speed and position signals are estimated by using the aforementioned sensorless control modules.usd ,usq are transformed into the stationary reference frame voltage commands us𝛼 ,us𝛽 , which are the inputs to the space vector modulation module for generating corresponding three-phase voltages. 2.2. Self-balancing control strategy based on optical electromechanically integrated closed-loop control For fast detection and tracking of yarn input and output signals, most of the existing weft accumulator drive systems adopt a compensation method based on an open-loop control. The detection response of yarn input and output signals is low [31–34], so it cannot realize high response adaptive tracking of yarn input and output signals [1–3]. For solving this problem, a self-balancing closed-loop control strategy for yarn input and output number and speed regulation based on integral separation PI regulator and integrated optical electromechanical detection is proposed in this paper. (1) Yarn input and output signal detection scheme based on optical and electromechanical integration.
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Fig. 11. The key components and entire apparatus of weft accumulator.
Fig. 12. The test platform.
The fast detection of the yarn input and output signal is the premise of this self-balancing closed-loop control strategy, and therefore a high response detection system based on optical and electromechanical integration is proposed as shown in Fig. 7. It consists of a controller PCB, a yarn input number and speed detection circuit, a yarn output number and a speed detection circuit, a yarn and winding shaft. At the two ends of the controller PCB, corresponding to the right and left ends of the winding shaft, the emission and feedback detection circuits are designed respectively to detect the yarn input and output signals based on photoelectric sensors. The yarn has a high input and output speed. For fast detection and tracking of yarn input and output signals, a new
high response yarn input number and speed detection circuit based on the principle of infrared reflection using optoelectronic devices is presented, as shown in Fig. 8. It consists of an infrared transmitting tube, an infrared receiving tube, a reflector, a conditioning circuit, a comparison circuit, and a constant current source drive. The infrared transmitting and receiving tubes consist of an infrared light emitting diode and a photosensitive triode. When the control board is powered on, the infrared light is continuously emitted by the infrared diode and reflected in the photosensitive triode through the reflector on the winding axis. The photosensitive triode receives infrared light and generates a corresponding electrical signal. When the yarn is moving at a high speed
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Mechatronics 62 (2019) 102249
Fig. 13. The experimental waveform of motor speed based on the two systems at a given speed of 100r/min.
Fig. 14. The experimental waveform of motor speed based on the two systems at a given speed of 1000r/min.
passing through the infrared emission path, the infrared light is absorbed by the yarn, and the infrared signal received by the photosensitive triode is weak, generating a different electrical signal. Therefore, a changing signal sequence can be obtained during the yarn winding process, and this signal is transmitted to the DSP together with the signals from the corresponding conditioning circuit and the comparison circuit. The rapid acquisition of yarn input and output signals is achieved. (2) Closed-loop control based on an integral separation PI regulator Based on the detected yarn input number and speed and yarn output number and speed, a closed-loop control strategy of yarn input and output using an integral separation PI regulator is presented, as shown in Fig. 9. The integral separation PI regulator can be expressed in (17), as shown in Fig. 10. Its design procedure is as follows: when the deviation between yarn output and yarn input is significant, to improve the stability of the system, the integral action is disabled. When the yarn input is close to the yarn output, the integral control is activated to reduce the steady-state error and improve the control accuracy; thus a balance between the yarn input and output can be realized. 𝑢(𝑘) = 𝑘𝑝 𝑒𝑟𝑟𝑜𝑟(𝑘) + 𝛽2 𝑘𝑖 Where:𝛽2 = {
1 0
𝑘 ∑ 𝑗=0
𝑒𝑟𝑟𝑜𝑟(𝑗)𝑇𝑠
|𝑒𝑟𝑟𝑜𝑟(𝑘) ≤ 𝜀| |𝑒𝑟𝑟𝑜𝑟(𝑘) > 𝜀|
(17)
3. Experimental test To verify the effectiveness of the proposed method and system, the induction motor, its controller and the whole machine of the weft accumulator is developed as shown in Fig. 11. A test platform is also developed, which consists of a doubler winder and a weft accumulator, as shown in Fig. 12, and the test is carried out using this platform. 3.1. Test and analysis of the proposed high-response sensorless vector control system According to the design requirements of the weft accumulator under desired operating conditions, the working speed of the induction motor is mainly between 100r/min and 3000r/min. For proving the superiority of the proposed induction motor sensorless vector control system based on the improved MRAC, the running speed of the motor is set at 100,1000,2000 and 3000r/min respectively, and the comparative tests with the traditional MRAC are carried out. The start-up performance and steady-state performance of the improved system and the traditional system are tested respectively when the reference speed of the motor is 100r/min. The measured speed profile of the motor is shown in Fig. 13. Fig. 13(a) shows the waveform of the sensorless vector control system designed by the traditional MRAC. It can be observed that the motor speed reaches the reference speed in about 0.6 ms. The maximum motor speed error in steady state is
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Fig. 15. The experimental waveform of motor speed based on the two systems at a given speed of 2000r/min.
Fig. 16. The experimental waveform of motor speed based on the two systems at a given speed of 3000r/min.
±5r/min. Fig. 13(b) shows the waveform of the sensorless vector control system designed by the improved MRAC. It can be observed that the motor reaches the reference speed in 0.4 ms, and the maximum motor speed error in steady state is ±0.2r/min. Therefore, compared with the traditional system, the new sensorless vector control system designed by the improved MRAC has faster start-up response and a smaller steadystate error than that offered by the traditional MRAC at a low speed of 100r/min. The test of the start-up performance and steady-state performance is repeated at 1000r/min, and the obtained results are shown in Fig. 14. Fig. 14(a) is the speed profile of the sensorless vector control system designed by the traditional MRAC. It can be observed that the motor start-up process takes about 5.6 ms, and the maximum speed error in the steady state is ±16r/min. Fig. 14(b) shows the waveform of the sensorless vector control system designed by the improved MRAC. It can be observed that the motor start-up process is reduced to 3.6 ms, and the maximum speed error in the steady state is ±0.6r/min. Much better start-up performance and smaller steady state speed error of the improved MRAC is achieved. Similar performance improvements are still achieved when the reference speed of the motor is 2000r/min, and the obtained experimental results are given in Fig. 15. Fig. 15(a) shows the waveform of the sensorless vector control system designed by the traditional MRAC, where the start-up takes about 11.6 ms, and the maximum speed error in steady state is ±20r/min. Fig. 15(b) shows the waveform of the sensorless vector control system designed by the improved MRAC, where the start-up process is reduced to 7.8 ms, and the maximum speed error in steady
state is ±0.5r/min. Therefore, compared with the traditional method, the improved MRAC has still faster start-up response and smaller steadystate error when running at a medium speed of 2000r/min. The experimental results at a high reference speed of 3000 r/min are given in Fig. 16. The start-up process takes about 18 ms with a steady state speed error of ±15r/min for the traditional MRAC while for the improved MRAC, the experimental results show a 12 ms start-up process and a steady state error of ±0.9r/min. Therefore, by comparing the experimental results shown in Fig. 17, it can be concluded that the sensorless vector control system designed by the improved MRAC proposed in this paper has faster start-up response and smaller steady-state error when it runs at different reference speeds, ranging from a low speed(100r/min) to a high speed(3000r/min). The maximum time for the motor to reach a steady state is about 12 ms when the reference speed is at its maximum of 3000r/min. Its performance can meet the requirement of the weft accumulator. 3.2. Test and analysis of the proposed self -balancing closed-loop control strategy The pulling speed of the doubler winder is set to be 200 m/min, and the driving performance of the weft accumulator is carried out in this pulling speed. The starting and steady-state operation performances are obtained, as shown in Fig. 18(a) and (b). It can be observed that the system can identify the yarn output speed quickly, which is 200 m/min when the yarn on the winding shaft of the weft accumulator is pulled out. The adaptive tracking speed that the motor can ensure balanced
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Fig. 17. Data of the two systems based on traditional MRAC and improved MRAC.
Fig. 18. The experimental waveforms of the system at a given speed of 200 m/min.
Fig. 19. The experimental waveforms of the system at a given speed of 300 m/min.
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Fig. 20. The experimental waveforms of the system at a given speed of 400 m/min.
input and output is 1000r/min. The motor starts to run and drives the speed control wheel, and the yarn is fed through the rotation of the speed control wheel. On the winding shaft, the system controls and adjusts the number and speed of yarn input and output in self-balanced condition based on the integral separation PI controller. The observed waveforms show that the yarn input speed is 200 m/min in the steady state operation condition, and the motor speed is 1000r/min, which agrees with the theoretical analysis. The system designed by this paper can follow the output speed of yarn well. The driving performance of the weft accumulator is tested when the pulling reference speed is 300 m/min and 400 m/min respectively, and the experimental starting and steady-state performances are shown in Figs. 19 and 20. It can be observed from the results that the number and speed of yarn input can follow the number and speed of yarn output well, which meets the requirement of the doubler winder under different reference speeds. The number and speed of the yarn input and output can work in self-balanced conditions. 4. Conclusion (1) The high-response and self-adaptive yarn delivery of the weft accumulator is the prerequisite for improving the efficiency and quality of the textile machinery. However, there are some problems in the current drive system of the weft accumulator, such as the slow start of the motor and slow tracking response. For solving these problems, a high-response and self-balancing sensorless integrated control system of the induction motor for weft accumulator is proposed. The two core technologies: the high-response sensorless vector control method based on an improved MRAC algorithm and the selfbalancing control strategy based on optical electromechanically integrated closed-loop control, are presented and analyzed. (2) For verifying the effectiveness of the proposed scheme, a test platform is built, and various experiments are carried out. The results show that compared with the traditional system, the proposed sensorless vector control system designed by the improved MRAC has faster starting response and smaller steady-state error when it runs at different given speeds. The designed yarn input and output detection scheme based on the optical and electromechanical integration can quickly identify the yarn input and output information, and the number and speed of yarn input can follow the output quickly based on the proposed integrated closed-loop control system. The proposed method can meet the requirements of high-response and self-balancing yarn delivery of the textile machinery.
Acknowledgments This work is supported by Zhejiang Provincial Natural Science Foundation of China (Grant no. LY18E070006, LY18E050016), Key Research and Development Projects of Zhejiang Science and Technology Department of China (Grant no. 2018C01074, 2018C01061), and National Natural Science Foundation of China (Grant no. 51677172). Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] Schneider D, Goly YS, Merhof D. Vision-based on-loom measurement of yarn densities in woven fabric. IEEE Trans Instrum Meas 2015;64:1063–74. https://ieeexplore.ieee.org/document/6945378/. [2] Gao MY, Zhong KF, He ZW. Design and realization of high-speed electronic weft-feeder control system. 2016 Sixth International Conference on Instrumentation and Measurement, Computer, Communication and Control, p. 584–588. https://doi.org/10.1109/ICCT.2015.7399825. [3] Kumar P, Mahendra SN. Alternative drive options for circular looms. In: Annual IEEE India Conference; 2015. p. 1–5. [4] Zhang XN, Foo GHB. A constant switching frequency-based direct torque control method for interior permanent-magnet synchronous motor drives. IEEE Trans Mechatron 2016;21:1145–56. https://ieeexplore.ieee.org/document/7273906/. [5] Baumgartner T, Johann WK. Multivariable state feedback control of a 500 000r/min self-bearing permanent-magnet motor. IEEE Trans Mechatron 2015;20:1149– 59. https://ieeexplore.ieee.org/document/6828713/. [6] Shanmugasundram R, Muhammad Zakariah K, Yadaiah N. Implementation and performance analysis of digital controllers for brushless DC motor drives. IEEE Trans Mechatron 2014;19:1213–24. https://ieeexplore.ieee.org/document/6365820/. [7] Nerat M, Vrančić D. A novel fast-filtering method for rotational speed of the BLDC motor drive applied to valve actuator. IEEE Trans Mechatron 2016;21:1479–86. https://ieeexplore.ieee.org/document/7346455/. [8] Zhang Z, Liu YQ, Bazzi AM. An improved high-performance open-loop V/f control method for induction machines. In: IEEE Applied Power Electronics Conference and Exposition; 2017. p. 615–19. [9] Liu KP, Xiao ZR, Niu XB. Research of varying frequency driving scheme for asynchronous induction coil launcher. IEEE Trans Plasma Sci 2017;45:1567–73. https://ieeexplore.ieee.org/document/7936592/. [10] Jia YJ, Rajashekara K. An induction generator-based AC/DC hybrid electric power generation system for more electric aircraft. IEEE Trans Ind Appl 2017;53. 2485-94 https://ieeexplore.ieee.org/document/7812661/. [11] Overboom TT, Smeets JPC, Jansen JW, Lomonova E. Decoupled control of thrust and normal force in a double-layer single-sided linear induction motor. Mechatronics 2012;23:213–21. https://doi.org/10.1016/j.mechatronics.2012.06.005. [12] Park GJ, Son B, Seo SH, Lee JH, Kim YJ, Jung SY. Compensation strategy of the numerical analysis in frequency domain on induction motor considering
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Mechatronics 2013;23:1150–62 https://doi.org/ 10.1016/j.mechatronics.2013.10.001. Zbede Y, Gadoue SM, Atkinson DJ. Predictive sensorless control of induction motor drives. Predictive sensorless control of induction motor drives. In: IEEE International Conference on Industrial Technology; 2015. p. 2339–44. Pan JF, Or SW, Zou Y, Cheung NC. Sliding-mode position control of medium-stroke voice coil motor based on system identification observer. IET Elect Power Appl 2015;9:620–7. https://ieeexplore.ieee.org/document/7328480/. Wang JG, Su JH, Lai JD, Zhao T. Study on digital implementation of full-order flux observer for induction motor. In: IEEE 8th International Power Electronics and Motion Control Conference; 2016. p. 842–5. Sun W, Yu Y, Wang GL, Xu DG. A novel design method for adaptive full order observer feedback matrix and speed estimation algorithm. In: IEEE Transactions on Industry Application Society Annual Meeting; 2014. p. 1–7. Jannati M, Anbaran S, Mohammad Zaheri D, Idris NRN, Aziz MJA. A new speed sensorless SVM-DTC in induction motor by using EKF. In: IEEE Student Conference on Research and Developement; 2013. p. 94–9. Soltani M, Bozorg M, Zakerzadeh MR. Parameter estimation of an SMA actuator model using an extended Kalman filter. Mechatronics 2018;50:148–59 https://doi.org/ 10.1016/j.mechatronics.2018.02.001. Zerdali E, Barut M. The comparisons of optimized extended kalman filters for speedsensorless control of induction motors. IEEE Trans Indust Electron 2017;64:4340–51. https://ieeexplore.ieee.org/document/7864354/. Rigatos GG. A derivative-free kalman filtering approach to state estimationbased control of nonlinear systems. IEEE Trans Indust Electron 2012;59:3987–97. https://ieeexplore.ieee.org/document/5892889/. Chiang HH, Hsu KC, Li IH. Optimized adaptive motion control through an SoPC implementation for linear induction motor drives. IEEE Trans Mechatron 2015;20:348– 60. https://ieeexplore.ieee.org/document/6800008/. Idriss B, Saïd D, Larbi CA, Mohammed O. Implementation of a new MRAS speed sensorless vector control of induction machine. IEEE Trans Energy Conv 2015;30:588– 95. https://ieeexplore.ieee.org/document/6960891/. Dris A, Bendjebbar M. Different technics of observation of the flux and the speed of an induction motor (KUBOTA and MRAS observers). In: 8th International Conference on Modelling, Identification and Control; 2016. p. 61–6. Kumar R, Das S, Syam P, Chattopadhyay AK. Review on model reference adaptive system for sensor lessvector control of induction motor drives. IET Elect Power Appl 2015;9:496–511. https://ieeexplore.ieee.org/document/7172605/. Smith AN, Gadoue SM, Finch JW. Improved rotor flux estimation at low speeds for torque MRAS-based sensorless induction motor drives. IEEE Trans Energy Conv 2016;31:270–82. https://doi.org/10.1109/TEC.2015.2480961. Hu Jun, Wu Bin. New integration algorithms for estimating motor flux over a wide speed range. IEEE Trans Power Electron 1998;13:969–77. https://doi.org/10.1109/PESC.1997.616875. Khanesar MA, Oniz Y, Kaynak O, Gao HJ. Direct model reference adaptive fuzzy control of networked SISO nonlinear systems. IEEE Trans Mechatron 2016;21:205– 13. https://ieeexplore.ieee.org/document/7321039/. Liem DT, Truong DQ, Ahn KK. A torque estimator using online tuning grey fuzzy PID for applications to torque-sensorless control of DC motors. Mechatronics 2015;26:45–63. http://orcid.org/0000-0002-7927-3348. Xiaoliang S, Jianli L, Yang XU, et al. On-line detecting system based on optoelectronic technology for ring spun-yarn breakage. J Text Res 2014;35. 94-0 https://ieeexplore.ieee.org/document/7726340/ . Shen DF, Zhao N, Zhang HA. Study of methods and reliability of weft signal detection with infrared photoelectric sensor. J Text Res 2012;33:75–9. https://doi.org/10.1128/JB.184.17.4800-4810.2002. Wenqi Lu received the B.S.degree from Zhejiang Ocean University, Zhejiang, China, in 2005, and the Ph.D.degree from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2011. In 2011, he was an Assistant Professor with the Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, where he has been an Associate Professor since 2017. From 2014 to 2017, he was a Post-Doctoral Researcher with the Department of Electrical Engineering, Zhejiang University. From 2017 to 2018, he was a guest researcher with the Department of Energy Technology, Aalborg University. His current research interests include the control of many motors, such as induction motor, brushless DC motor, and permanent magnet synchronous motor, and its application in the robot and high-end equipment.
Mechatronics 62 (2019) 102249 Di Wu received the B.S.degree from Zhejiang Sci-Tech University, Zhejiang, China, in 2017. He is currently pursuing a master degree in control science and engineering, Zhejiang Sci-Tech University, Hangzhou, China. His research interests include the design of the robot motor driver, permanent magnet AC servo system.
Yansuo Zhou received the B.S. degree in Mechanical Manufacture and Automation from Henan Polytechnic University, Henan, China, in 2006, and the M.S. degree from Hong Kong Polytechnic University, Hong Kong, China, in 2016. He is currently pursuing a Ph.D. degree in advanced manufacturing from Tianjin University. From 2006 to 2010, he was an engineer worked in Hangzhou Advance Gearbox Group Co., Ltd. Hangzhou, China. From 2011 to 2017, he was a senior researcher worked in Zhejiang Electrical Group Co., Ltd. His research interests include the automatic control technology and control of synchronous reluctance and permanent magnet machines.
Kaiyuan Lu (M’11) received the B.S. and M.S. degrees from Zhejiang University, Zhejiang, China, in 1997 and 2000 respectively, and the Ph.D. degree from Aalborg University, Denmark, in 2005, all in electrical engineering. In 2005, he became an Assistance Professor with the Department of Energy Technology, Aalborg University, where he has been an Associate Professor since 2008. His research interests include the design of permanent magnet machines, finite element method analysis, and control of permanent magnet machines.
Dong Wang (S’13-M’16) received the B.S. degree from Zhejiang University, Zhejiang, China, in 2004, and the M.S. and Ph.D. degrees from Aalborg University, Denmark, in 2006 and 2016, respectively, all in electrical engineering. From 2006 to 2012, he was with Grundfos R&D China, Suzhou, China, as a Senior Motor Engineer, working on the design and analysis of the permanent magnet machine and devices. From 2016 to 2017, he was a Postdoc Researcher with the Department of Energy Technology, Aalborg University, where he has been an Assistant Professor since 2017. His research interests include design and control of synchronous reluctance and permanent magnet machines.
Hao Wu is currently pursuing the B.S.degree at Mechanical and Electronic Engineering, Zhejiang Sci-Tech University, Hangzhou, China. His research interests include the control of the asynchronous motor and its application in the robot and high-end equipment.
Liangliang Yang received the B.S. degree in vehicle engineering from Chongqing University of Technology, Chongqing, China, in 2001, and the M.S. and Ph.D. degrees in Mechatronic Engineering from Huazhong University of Science & Technology, Wuhan, in 2005 and 2009, respectively. From 2009 to 2015, he worked as a Lecture in Zhejiang Sci-Tech University, Hangzhou, China. From 2015 to Now as an Associate professor. Principally on the design and analysis of the high speed and high precision motion control system. His research interests include the design and control of high-speed and high-precision motion control system.
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A new high-response self-balancing sensorless control system of induction motor for weft accumulator ✩ Wenqi Lu a,1,∗, Di Wu a, Yansuo Zhou b, Kaiyuan Lu c,∗, Dong Wang c, Hao Wu a, Liangliang Yang a a
Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China Faculty of Mechanical Engineering, Tianjin University, Tianjin 300072, China c Department of Energy Technology, Aalborg University, Aalborg DK-9220, Denmark b
a r t i c l e
i n f o
Keywords: Weft accumulator High-response Self-balancing Sensorless Model reference adaptive control Optical electromechanically integrated
a b s t r a c t The high-response and self-adaptive yarn delivery system used in weft accumulator is a critical component for improving the efficiency and quality of textile machinery. However, the existing drive system in the weft accumulator has problems such as slow start-up and slow speed-tracking responses, giving difficulties in achieving a high-efficient yarn delivery of the textile machinery. In this paper, a high-response self-balancing sensorless integrated control system of the induction motor for weft accumulator is proposed. Firstly, to enable the motor to start and respond adaptively and quickly to the number of yarn control loops and the speed control of the external equipment, a high-response sensorless vector control system for induction motor based on improved model reference adaptive control(MRAC) is proposed. Secondly, to achieve a high-response adaptive tracking performance of yarn input and output numbers and spool shaft speed, a self-balancing control strategy based on optical electromechanically integrated closed-loop control is proposed. For proving the effectiveness of the proposed scheme, a corresponding test platform was constructed for testing and validation. It can be observed from the results that the system designed in this paper has fast starting speed and better speed tracking performance, satisfying yarn delivery requirements at different speeds with high-response and adaptability.
1. Introduction Weft accumulator is a temporary storage device of the unwinding weft yarn on the cylinder before it is introduced into the shuttle. It is used in the rapier loom, shuttle loom, water jet loom and air jet loom, etc. The temporary weft yarns are arranged neatly and wound on the smooth cylinder or cone surface, which creates a good condition for weft insertion at high speed. It also allows the unwinding yarn to obtain more uniform tension, thus avoiding large tension fluctuation during the unwinding of yarn and reducing yarn failure consequently. It has become the critical equipment for many textile production lines, and its performance directly affects the efficiency and quality of the entire production line. In such a system, the variable frequency motor drive performance and the fast detection and tracking ability of yarn input and output signals are two core technologies of the weft accumulator, which directly determine whether the weft accumulator can meet the requirements with respect to yarn delivery tension and production speed of different textile machinery [1–3]. It has become a hot topic to study in this field.
✩ ∗
1
Currently, there are three kinds of drive systems for variable frequency motor drives: induction motor drives, permanent magnet synchronous motor drives, and brushless DC motor drives. References [4,5] adopted a permanent magnet synchronous motor with a vector control drive system. The designed motor control system has demonstrated good speed response, and can effectively meet the control requirements for weft accumulator motor. But the cost of the permanent magnet synchronous motor drive system is very high. It is more suitable for cost-insensitive applications, not for cost-efficient applications like for the weft accumulator. References [6,7] adopted a brushless DC motor sensorless control system. Torque ripple caused by current commutation and slot effect based on vector control of the Brushless DC motor was studied in these papers. A speed control scheme using improved space vector pulse width modulation (SVPWM) and possible cogging torque ripple compensation method were proposed. But in practical operations, the motor of the weft accumulator needs a quick start and stop function. The brushless DC motor sensorless drive system has poor controllability in the initial start-up process and has difficulty in satisfying the fast start-up requirement for the weft accumulator. Compared with the
This paper was recommended for publication by Associate Editor Dr. Marcel Francois Heertjes. Corresponding authors. E-mail addresses: [email protected] (W. Lu), [email protected] (K. Lu). 23#327, Zhejiang Sci-Tech University, 928 Second Avenue, Xiasha Higher Education Zone, Hangzhou, Zhejiang, PR China.
https://doi.org/10.1016/j.mechatronics.2019.07.001 Received 10 September 2018; Received in revised form 25 June 2019; Accepted 5 July 2019 0957-4158/© 2019 Elsevier Ltd. All rights reserved.
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two kinds of motor drive systems mentioned above, the induction motor drive system has good controllability in the initial start-up process, and its cost is low. The induction motor drive system is considered as a more suitable candidate for the weft accumulator. Many control strategies have been proposed for induction motor drive systems. References [8–10] presented a simple control strategy with a constant voltage to frequency ratio for a three-phase induction motor. It has advantages of simple hardware implementation, good stability and steady-state performance. But this control strategy is an open-loop control method, which has problems of low speed regulation accuracy and poor dynamic performance. References [11,12] presented a slip frequency control scheme derived from the rotating stator magnetic field oriented induction machine model. The slip frequency is minimal when the motor is at a steady state. The torque of the motor can then be approximated to be proportional to the slip angular frequency while maintaining a constant rotor flux linkage. Therefore, the torque of the motor can be controlled by simply controlling the slip angular frequency, and the speed can be controlled and adjusted consequently. However, the most significant disadvantage of this control method is that it is only suitable for situations where the speed is constant or the speed changes slowly. References [13,14] presented a direct torque control method, which chooses the appropriate voltage space vector directly according to the position of the stator flux, the deviation of stator flux amplitude and the deviation of the electromagnetic torque to realize a direct control of the torque. It exhibits good starting torque and fast dynamic response. However, this control scheme results in large torque ripple and large steady-state error. It is suitable for small speed range and low steady-state accuracy applications. Moreover, these control methods usually use motor shaft sensors to measure real motor speed. The cost of such a drive system with a sensor is high, and the application is restricted since how to mechanically integrate the shaft sensor to the motor is a challenge for many compact drive system applications. If the speed sensorless control strategy can be adopted, this problem can be avoided. In recent years, a large number of literatures have studied speed sensorless control strategies for induction motors. References [15,16] presented a speed identification method based on stator and rotor flux linkage and angular slip speed. It is simple and easy to implement, but the motor flux linkage and other parameters of the motor are needed. The speed estimation accuracy depends on the motor parameters which could change in different working conditions. Besides, the speed identification process is open-loop control, so there is no error correction measure. References [17–19] presented a speed identification method based on a full-order state observer. It does not depend on the induction motor parameters, but this method is complex and challenging to implement in practical DSP-based applications. References [20–23] presented a method using an extended Kalman filter. The advantage of this method is its ability to reduce the influence of measurement noise and random interference signals effectively. But this method is based on the known errors and measured noise statistics. For different motors, the corresponding measured noise statistics are needed to calibrate the motor characteristic parameters so that the practicability and adaptability of this method are poor. References [24–28] presented a method based on a model reference adaptive system. According to the designed adaptive law, the motor parameters are identified by forcing the error of the reference model and the output of the adjustable model to zero. It has advantages of high robustness and simple realization. But the reference model has problems of slow convergence speed, limited variable speed range, integral drift and initial value due to the influence of stator resistive voltage drop and the use of a pure integrator. As a result, the starting response of the motor is slow, which cannot meet the high efficient yarn delivery requirement for weft accumulator. For fast detection and tracking of yarn input and output signals, most of the existing weft accumulator drive systems adopt the compensation method based on an open-loop control, and the detection speed of yarn input and output signals is slow. It has difficulty in realizing a high adaptive tracking performance of yarn input and output signals [1–3].
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Therefore, firstly, to improve the starting speed and speed-tracking performance of the motor, a high-response, vector control system based on the improved model reference adaptive control (MRAC) algorithm for the induction motor without position sensor is proposed. Secondly, for improving the speed-tracking performance of the yarn input and output, a self-balancing control strategy is proposed based on an optical electromechanically integrated closed-loop control system. Finally, by integrating the above two methods, a high-response and self-balancing sensorless induction motor control system for weft accumulator is proposed. To verify the effectiveness of the design method and the developed system, besides the theoretical analysis, prototype design and experimental tests are carried out. In this paper, in section II, the design principle of a high-response self-balancing sensorless integrated control system of the induction motor for weft accumulator is presented. The proposed new method includes two solutions. First, a high-response sensorless vector control strategy based on an improved model reference adaptive algorithm is discussed in section II.1. Secondly, a self-balancing control strategy of yarn input and output based on an optical electromechanically integrated closed-loop control is given in section II.2. In section III, the experimental validation is carried out. Section IV concludes this paper. 2. The proposed new high-response and self-balancing sensorless integrated control system The structural block diagram of the weft accumulator studied in this paper is shown in Fig. 1, where optoelectronics and electromechanics are highly integrated. It consists of a yarn input port, a roof, an induction motor, a speed regulating wheel, a controller, a yarn input detection circuit, a yarn output detection circuit, a yarn winding mechanism, a yarn output port and other supporting parts. The working principle of the weft accumulator is described as follows: the yarn output detection circuit measures the yarn output number and its sending speed; the obtained speed is taken as the reference speed for the controller. The induction motor will then drive the speed regulating wheel and the winding shaft, pulling the yarn from the input port to the winding shaft. At the same time, the yarn input detection circuit measures the yarn input number and speed, and the self-balancing control of the yarn input and output number and speed needs to be implemented. In such a system, the variable frequency motor drive performance (fast start-up and speed regulation ability) and fast detection and tracking capability of yarn input and output signals are the two core technologies of this control system. For improving the speed tracking ability of yarn input and output and reduce the motor losses, a new high dynamic response and selfbalancing sensorless integrated control system are proposed in this paper, as shown in Fig. 2. It includes two solutions. Firstly, a high-response sensorless vector control strategy based on the improved model reference adaptive algorithm is proposed for the induction motor drive system. Secondly, a self-balancing control strategy of yarn input and output based on an optical electromechanically integrated closed-loop control is proposed. The two solutions are discussed in detail below.
Fig. 1. The structural block diagram of weft accumulator.
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Fig. 2. The principal block diagram of a new high-response and self-balancing sensorless integrated control system.
𝑢𝑠𝛽 = 𝑅𝑠 𝑖𝑠𝛽 +
𝑑 𝜓𝑠𝛽
𝑢𝑟𝛼 = 0 = 𝑅𝑟 𝑖𝑟𝛼 + 𝑢𝑟𝛽 = 0 = 𝑅𝑟 𝑖𝑟𝛽 +
Fig. 3. The basic composition structure of traditional MRAC.
2.1. High-response sensorless vector control strategy of induction motor based on an improved MRAC The traditional MRAC method [24–28] for sensorless speed identification of induction motors usually consists of three parts: a reference model, an adjustable model and an adaptive algorithm. Its principle block diagram is shown in Fig. 3. The reference model is generally used to calculate the actual rotor flux and its angle, which is obtained by a voltage model using a pure integrator. The adjustable model is used to estimate the rotor flux, which utilizes a current model. The adaptive algorithm is used to calculate the rotor speed, by using a PI regulator with fixed coefficients. The voltage model has problems of slow convergence speed, limited variable speed range, integral drift and initial value due to the influence of stator resistive voltage drop and the use of a pure integrator [24–28]. An improved rotor flux observer based on a first-order inertial filter with rotor flux compensation is adopted in this paper, to overcome the integral drift and initial value problems [29,30]. The problems of slow convergence speed and limited speed range are alleviated by using a PI regulator with adaptive coefficients. The structural block diagram of the improved MRAC is shown in Fig. 4. The following is a description of its design principles. (1) The rotor flux and angle estimator based on an improved voltage model In the stationary reference frame, the stator voltage equation, rotor voltage equation, flux linkage equation and the electromagnetic torque equation of an induction motor can be obtained as follows: 𝑢𝑠𝛼 = 𝑅𝑠 𝑖𝑠𝛼 +
𝑑 𝜓𝑠𝛼 𝑑𝑡
(1)
(2)
𝑑𝑡 𝑑 𝜓𝑟𝛼 + 𝜔𝑟 𝜓𝑟𝛽 𝑑𝑡 𝑑 𝜓𝑟𝛽 𝑑𝑡
(3)
− 𝜔𝑟 𝜓𝑟𝛼
(4)
𝜓𝑠𝛼 = 𝐿𝑠 𝑖𝑠𝛼 + 𝐿𝑚 𝑖𝑟𝛼
(5)
𝜓𝑠𝛽 = 𝐿𝑠 𝑖𝑠𝛽 + 𝐿𝑚 𝑖𝑟𝛽
(6)
𝜓𝑟𝛼 = 𝐿𝑟 𝑖𝑟𝛼 + 𝐿𝑚 𝑖𝑠𝛼
(7)
𝜓𝑟𝛽 = 𝐿𝑟 𝑖𝑟𝛽 + 𝐿𝑚 𝑖𝑠𝛽
(8)
𝑇𝑒 =
3 𝐿𝑚 𝑝 (𝜓 𝑖 − 𝜓𝑠𝛽 𝑖𝑠𝛼 ) 2 𝑝 𝐿𝑟 𝑠𝛼 𝑠𝛽
(9)
In a rotating d-q reference frame, the current model of the motor rotor flux can be obtained as follows: 𝐿𝑚 𝑖 − 𝑇𝑟 𝑠𝑑 𝐿𝑚 = 𝑖 − 𝑇𝑟 𝑠𝑞
𝑝𝜓𝑟𝑑 = 𝑝𝜓𝑟𝑞
1 𝜓 − j(𝜔𝑠 − 𝜔𝑟 )𝜓𝑟𝑞 𝑇𝑟 𝑟𝑑 1 𝜓 − j(𝜔𝑠 − 𝜔𝑟 )𝜓𝑟𝑑 𝑇𝑟 𝑟𝑞
(10)
The rotor back EMF can be obtained as follows: 𝐿𝑟 (𝑢 − 𝑅𝑠 𝑖𝑠𝛼 − 𝜎𝐿𝑠 𝑝𝑖𝑠𝛼 ) 𝐿𝑚 𝑠𝛼 𝐿𝑟 = (𝑢 − 𝑅𝑠 𝑖𝑠𝛽 − 𝜎𝐿𝑠 𝑝𝑖𝑠𝛽 ) 𝐿𝑚 𝑠𝛽
𝑒𝑟𝛼 = 𝑝𝜓𝑟𝛼 = 𝑒𝑟𝛽 = 𝑝𝜓𝑟𝛽
(11)
where, us𝛼 , us𝛽 is the stator winding resistance and rotor winding resistance respectively, us𝛼 ,us𝛽 ,ur𝛼 ,ur𝛽 is the 𝛼 and 𝛽axis components of the stator and rotor voltage respectively, is𝛼 ,is𝛽 , ir𝛼 , ir𝛽 is the 𝛼and𝛽axis components of the stator and rotor current respectively, Ls ,Lr ,Lm is the stator inductance, rotor inductance and mutual inductance between stator and rotor respectively, 𝜓 s𝛼 ,𝜓 s𝛽 ,𝜓 r𝛼 ,𝜓 r𝛽 is the 𝛼and𝛽axis component of stator and rotor flux respectively,𝜓 rd ,𝜓 rd is the d and q axis component of rotor flux respectively,Te is the electromagnetic torque, pp is the number 𝜏 of pole-pairs, 𝜔r is the rotor actual velocity, 𝜓𝑠𝛽 (𝑘) = 𝜏 +1𝑇 [𝜓𝑠𝛽 (𝑘 − 1) + 1
𝑠
𝑇𝑠 (𝑢𝑠𝛽 (𝑘) − 𝑅𝑠 𝑖𝑠𝛽 (𝑘))] is the magnetic flux leakage coefficient,er𝛼 ,er𝛽 are the rotor back EMF,p is the differentiation operator, Tr is the rotor time constant,𝜔s is the rotating speed of the stator magnetic field.
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Fig. 4. The basic composition structure of improved MRAC.
Fig. 5. The rotor flux estimation based on improved voltage model method.
Fig. 6. The principle block diagram of the high-response sensorless vector control system of induction motor based on an improved MRAC algorithm.
Fig. 7. The schematic diagram of yarn input and output detection system based on optical electromechanically integrated.
In order to solve the integral drift and initial value problems, an improved rotor flux observer using a first-order inertial filter is adopted [29,30]; for reducing the amplitude and phase error of the estimated flux and improving the dynamic response of the motor, the rotor flux current model in the rotor flux oriented reference frame is used to compensate the error. The first-order inertial filter is an effective combination of a
Fig. 8. The schematic diagram of yarn input and output number and speed detection circuit based on optoelectronic devices.
pure integrator and a first-order high-pass filter. The complete schematic block diagram of the rotor flux estimator is shown in Fig. 5.
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Fig. 9. The principal block diagram of the closed-loop control strategy based on an integral separation PI regulator.
When the rotor speed is low (smaller than 200r/min) , to improve the dynamic response of the system and reduce the convergence time, the value of kpm , kim is chosen to be large values, and their values are fixed (𝑘𝑝m =𝐾𝑝 ,𝑘im = 𝐾𝑖 ). When the rotor speed increases and is higher than 200r/min, to reduce the steady-state error and reduce the overshoot and settling time, the coefficients kpm , kim are chosen to be rotor speed dependent values as described in (14) and (15). { 𝐾𝑝 𝜔cmd < 200 𝑘𝑝m = (14) 𝐾𝑝 𝜔200 𝜔cmd ≥ 200 cmd { 𝐾𝑖 𝜔cmd < 200 𝑘im = (15) 𝐾𝑖 − 𝐾0 (𝜔cmd − 200) 𝜔cmd ≥ 200 where, Kp and Ki are constant values; K0 is the slope coefficient, and 𝜔cmd is the given rotor speed. Finally, the rotor estimated speed 𝜔̂ 𝑟 can be calculated by: 𝜔̂ 𝑟 =
𝑡
∫0
𝑘im (𝜓̂ 𝑟𝛽 𝜓𝑟𝛼1 − 𝜓̂ 𝑟𝛼 𝜓𝑟𝛽1 )𝑑𝑡 + 𝑘𝑝m (𝜓̂ 𝑟𝛽 𝜓𝑟𝛼1 − 𝜓̂ 𝑟𝛼 𝜓𝑟𝛽1 )
(16)
(3) A sensorless vector control system based on the improved MRAC
Fig. 10. The integral separation PI regulator.
Finally, the rotor flux may be estimated by: 𝜓𝑟1 =
𝑇𝑐 𝑝𝜓𝑟 +𝜓𝑟∗ 𝑇𝑐 1 1 𝑒𝑟 + 𝜓𝑟∗ = = 𝜓𝑟 +(𝜓𝑟∗ − 𝜓𝑟 ) 1 + 𝑇𝑐 𝑝 1 + 𝑇𝑐 𝑝 1 + 𝑇𝑐 𝑝 1 + 𝑇𝑐 𝑝
(12)
where, Tc is the cutoff frequency of the filter. According to (12), the actual flux of the rotor is equal to the reference flux, when the system is in steady state, which means 𝜓𝑟 = 𝜓𝑟∗ and the error is zero. 𝜓𝑟1 = 𝜓𝑟 = 𝜓𝑟∗ is also valid, which means the improved voltage model can realize zero error of flux magnitude and phase estimations in steady-state operation. When the system is running in dynamic conditions, the initial flux𝜓𝑟 ≠ 𝜓𝑟∗ , but 𝜓 r will converge to 𝜓𝑟∗ asymptotically. Its convergence speed is related to the time constant Tc . According to (12), the rotor flux angle can be calculated as follows. ( 𝜃𝜓𝑟 = arctan
𝜓𝑟𝛽1 𝜓𝑟𝛼1
) (13)
(2) The rotor speed estimator based on an adaptive coefficient PI regulator This paper adopts an improved voltage model to solve the integral drift and initial value problems caused by a pure integrator. However, due to the influence of stator resistance, the convergence speed of the reference model is slow and large speed estimation error when running at low speed may occur. An adaptative parameter PI regulator is then proposed to solve this problem. The proportional gain kpm and integrator coefficient kim of the PI regulator is adjusted according to (14) and (15).
Based on the above analysis, a high-response sensorless vector control system for induction motor based on the improved MRAC algorithm is proposed in this paper, as shown in Fig. 6. It consists of a speed control loop, a current control loop, the improved MRAC, a SVPWM, a threephase inverter, and an induction motor, etc. In this system, the outer speed control loop uses the estimated speed obtained by the observer based on the improved MRAC for speed regulation. The speed error is fed into a speed regulator, which generates the quadrature axis current command 𝑖𝑠𝑞 _𝑟𝑒𝑓 for the inner current control loop. The quadrature axis current command 𝑖𝑠𝑞 _𝑟𝑒𝑓 and its actual feedback value isq are inputs into the quadrature axis current regulator, and then the q-axis voltage command usq is generated. The d-axis voltage command usd is generated similarly. Speed and position signals are estimated by using the aforementioned sensorless control modules.usd ,usq are transformed into the stationary reference frame voltage commands us𝛼 ,us𝛽 , which are the inputs to the space vector modulation module for generating corresponding three-phase voltages. 2.2. Self-balancing control strategy based on optical electromechanically integrated closed-loop control For fast detection and tracking of yarn input and output signals, most of the existing weft accumulator drive systems adopt a compensation method based on an open-loop control. The detection response of yarn input and output signals is low [31–34], so it cannot realize high response adaptive tracking of yarn input and output signals [1–3]. For solving this problem, a self-balancing closed-loop control strategy for yarn input and output number and speed regulation based on integral separation PI regulator and integrated optical electromechanical detection is proposed in this paper. (1) Yarn input and output signal detection scheme based on optical and electromechanical integration.
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Fig. 11. The key components and entire apparatus of weft accumulator.
Fig. 12. The test platform.
The fast detection of the yarn input and output signal is the premise of this self-balancing closed-loop control strategy, and therefore a high response detection system based on optical and electromechanical integration is proposed as shown in Fig. 7. It consists of a controller PCB, a yarn input number and speed detection circuit, a yarn output number and a speed detection circuit, a yarn and winding shaft. At the two ends of the controller PCB, corresponding to the right and left ends of the winding shaft, the emission and feedback detection circuits are designed respectively to detect the yarn input and output signals based on photoelectric sensors. The yarn has a high input and output speed. For fast detection and tracking of yarn input and output signals, a new
high response yarn input number and speed detection circuit based on the principle of infrared reflection using optoelectronic devices is presented, as shown in Fig. 8. It consists of an infrared transmitting tube, an infrared receiving tube, a reflector, a conditioning circuit, a comparison circuit, and a constant current source drive. The infrared transmitting and receiving tubes consist of an infrared light emitting diode and a photosensitive triode. When the control board is powered on, the infrared light is continuously emitted by the infrared diode and reflected in the photosensitive triode through the reflector on the winding axis. The photosensitive triode receives infrared light and generates a corresponding electrical signal. When the yarn is moving at a high speed
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Fig. 13. The experimental waveform of motor speed based on the two systems at a given speed of 100r/min.
Fig. 14. The experimental waveform of motor speed based on the two systems at a given speed of 1000r/min.
passing through the infrared emission path, the infrared light is absorbed by the yarn, and the infrared signal received by the photosensitive triode is weak, generating a different electrical signal. Therefore, a changing signal sequence can be obtained during the yarn winding process, and this signal is transmitted to the DSP together with the signals from the corresponding conditioning circuit and the comparison circuit. The rapid acquisition of yarn input and output signals is achieved. (2) Closed-loop control based on an integral separation PI regulator Based on the detected yarn input number and speed and yarn output number and speed, a closed-loop control strategy of yarn input and output using an integral separation PI regulator is presented, as shown in Fig. 9. The integral separation PI regulator can be expressed in (17), as shown in Fig. 10. Its design procedure is as follows: when the deviation between yarn output and yarn input is significant, to improve the stability of the system, the integral action is disabled. When the yarn input is close to the yarn output, the integral control is activated to reduce the steady-state error and improve the control accuracy; thus a balance between the yarn input and output can be realized. 𝑢(𝑘) = 𝑘𝑝 𝑒𝑟𝑟𝑜𝑟(𝑘) + 𝛽2 𝑘𝑖 Where:𝛽2 = {
1 0
𝑘 ∑ 𝑗=0
𝑒𝑟𝑟𝑜𝑟(𝑗)𝑇𝑠
|𝑒𝑟𝑟𝑜𝑟(𝑘) ≤ 𝜀| |𝑒𝑟𝑟𝑜𝑟(𝑘) > 𝜀|
(17)
3. Experimental test To verify the effectiveness of the proposed method and system, the induction motor, its controller and the whole machine of the weft accumulator is developed as shown in Fig. 11. A test platform is also developed, which consists of a doubler winder and a weft accumulator, as shown in Fig. 12, and the test is carried out using this platform. 3.1. Test and analysis of the proposed high-response sensorless vector control system According to the design requirements of the weft accumulator under desired operating conditions, the working speed of the induction motor is mainly between 100r/min and 3000r/min. For proving the superiority of the proposed induction motor sensorless vector control system based on the improved MRAC, the running speed of the motor is set at 100,1000,2000 and 3000r/min respectively, and the comparative tests with the traditional MRAC are carried out. The start-up performance and steady-state performance of the improved system and the traditional system are tested respectively when the reference speed of the motor is 100r/min. The measured speed profile of the motor is shown in Fig. 13. Fig. 13(a) shows the waveform of the sensorless vector control system designed by the traditional MRAC. It can be observed that the motor speed reaches the reference speed in about 0.6 ms. The maximum motor speed error in steady state is
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Fig. 15. The experimental waveform of motor speed based on the two systems at a given speed of 2000r/min.
Fig. 16. The experimental waveform of motor speed based on the two systems at a given speed of 3000r/min.
±5r/min. Fig. 13(b) shows the waveform of the sensorless vector control system designed by the improved MRAC. It can be observed that the motor reaches the reference speed in 0.4 ms, and the maximum motor speed error in steady state is ±0.2r/min. Therefore, compared with the traditional system, the new sensorless vector control system designed by the improved MRAC has faster start-up response and a smaller steadystate error than that offered by the traditional MRAC at a low speed of 100r/min. The test of the start-up performance and steady-state performance is repeated at 1000r/min, and the obtained results are shown in Fig. 14. Fig. 14(a) is the speed profile of the sensorless vector control system designed by the traditional MRAC. It can be observed that the motor start-up process takes about 5.6 ms, and the maximum speed error in the steady state is ±16r/min. Fig. 14(b) shows the waveform of the sensorless vector control system designed by the improved MRAC. It can be observed that the motor start-up process is reduced to 3.6 ms, and the maximum speed error in the steady state is ±0.6r/min. Much better start-up performance and smaller steady state speed error of the improved MRAC is achieved. Similar performance improvements are still achieved when the reference speed of the motor is 2000r/min, and the obtained experimental results are given in Fig. 15. Fig. 15(a) shows the waveform of the sensorless vector control system designed by the traditional MRAC, where the start-up takes about 11.6 ms, and the maximum speed error in steady state is ±20r/min. Fig. 15(b) shows the waveform of the sensorless vector control system designed by the improved MRAC, where the start-up process is reduced to 7.8 ms, and the maximum speed error in steady
state is ±0.5r/min. Therefore, compared with the traditional method, the improved MRAC has still faster start-up response and smaller steadystate error when running at a medium speed of 2000r/min. The experimental results at a high reference speed of 3000 r/min are given in Fig. 16. The start-up process takes about 18 ms with a steady state speed error of ±15r/min for the traditional MRAC while for the improved MRAC, the experimental results show a 12 ms start-up process and a steady state error of ±0.9r/min. Therefore, by comparing the experimental results shown in Fig. 17, it can be concluded that the sensorless vector control system designed by the improved MRAC proposed in this paper has faster start-up response and smaller steady-state error when it runs at different reference speeds, ranging from a low speed(100r/min) to a high speed(3000r/min). The maximum time for the motor to reach a steady state is about 12 ms when the reference speed is at its maximum of 3000r/min. Its performance can meet the requirement of the weft accumulator. 3.2. Test and analysis of the proposed self -balancing closed-loop control strategy The pulling speed of the doubler winder is set to be 200 m/min, and the driving performance of the weft accumulator is carried out in this pulling speed. The starting and steady-state operation performances are obtained, as shown in Fig. 18(a) and (b). It can be observed that the system can identify the yarn output speed quickly, which is 200 m/min when the yarn on the winding shaft of the weft accumulator is pulled out. The adaptive tracking speed that the motor can ensure balanced
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Fig. 17. Data of the two systems based on traditional MRAC and improved MRAC.
Fig. 18. The experimental waveforms of the system at a given speed of 200 m/min.
Fig. 19. The experimental waveforms of the system at a given speed of 300 m/min.
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Fig. 20. The experimental waveforms of the system at a given speed of 400 m/min.
input and output is 1000r/min. The motor starts to run and drives the speed control wheel, and the yarn is fed through the rotation of the speed control wheel. On the winding shaft, the system controls and adjusts the number and speed of yarn input and output in self-balanced condition based on the integral separation PI controller. The observed waveforms show that the yarn input speed is 200 m/min in the steady state operation condition, and the motor speed is 1000r/min, which agrees with the theoretical analysis. The system designed by this paper can follow the output speed of yarn well. The driving performance of the weft accumulator is tested when the pulling reference speed is 300 m/min and 400 m/min respectively, and the experimental starting and steady-state performances are shown in Figs. 19 and 20. It can be observed from the results that the number and speed of yarn input can follow the number and speed of yarn output well, which meets the requirement of the doubler winder under different reference speeds. The number and speed of the yarn input and output can work in self-balanced conditions. 4. Conclusion (1) The high-response and self-adaptive yarn delivery of the weft accumulator is the prerequisite for improving the efficiency and quality of the textile machinery. However, there are some problems in the current drive system of the weft accumulator, such as the slow start of the motor and slow tracking response. For solving these problems, a high-response and self-balancing sensorless integrated control system of the induction motor for weft accumulator is proposed. The two core technologies: the high-response sensorless vector control method based on an improved MRAC algorithm and the selfbalancing control strategy based on optical electromechanically integrated closed-loop control, are presented and analyzed. (2) For verifying the effectiveness of the proposed scheme, a test platform is built, and various experiments are carried out. The results show that compared with the traditional system, the proposed sensorless vector control system designed by the improved MRAC has faster starting response and smaller steady-state error when it runs at different given speeds. The designed yarn input and output detection scheme based on the optical and electromechanical integration can quickly identify the yarn input and output information, and the number and speed of yarn input can follow the output quickly based on the proposed integrated closed-loop control system. The proposed method can meet the requirements of high-response and self-balancing yarn delivery of the textile machinery.
Acknowledgments This work is supported by Zhejiang Provincial Natural Science Foundation of China (Grant no. LY18E070006, LY18E050016), Key Research and Development Projects of Zhejiang Science and Technology Department of China (Grant no. 2018C01074, 2018C01061), and National Natural Science Foundation of China (Grant no. 51677172). Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] Schneider D, Goly YS, Merhof D. Vision-based on-loom measurement of yarn densities in woven fabric. IEEE Trans Instrum Meas 2015;64:1063–74. https://ieeexplore.ieee.org/document/6945378/. [2] Gao MY, Zhong KF, He ZW. Design and realization of high-speed electronic weft-feeder control system. 2016 Sixth International Conference on Instrumentation and Measurement, Computer, Communication and Control, p. 584–588. https://doi.org/10.1109/ICCT.2015.7399825. [3] Kumar P, Mahendra SN. Alternative drive options for circular looms. In: Annual IEEE India Conference; 2015. p. 1–5. [4] Zhang XN, Foo GHB. A constant switching frequency-based direct torque control method for interior permanent-magnet synchronous motor drives. IEEE Trans Mechatron 2016;21:1145–56. https://ieeexplore.ieee.org/document/7273906/. [5] Baumgartner T, Johann WK. Multivariable state feedback control of a 500 000r/min self-bearing permanent-magnet motor. IEEE Trans Mechatron 2015;20:1149– 59. https://ieeexplore.ieee.org/document/6828713/. [6] Shanmugasundram R, Muhammad Zakariah K, Yadaiah N. Implementation and performance analysis of digital controllers for brushless DC motor drives. IEEE Trans Mechatron 2014;19:1213–24. https://ieeexplore.ieee.org/document/6365820/. [7] Nerat M, Vrančić D. A novel fast-filtering method for rotational speed of the BLDC motor drive applied to valve actuator. IEEE Trans Mechatron 2016;21:1479–86. https://ieeexplore.ieee.org/document/7346455/. [8] Zhang Z, Liu YQ, Bazzi AM. An improved high-performance open-loop V/f control method for induction machines. In: IEEE Applied Power Electronics Conference and Exposition; 2017. p. 615–19. [9] Liu KP, Xiao ZR, Niu XB. Research of varying frequency driving scheme for asynchronous induction coil launcher. IEEE Trans Plasma Sci 2017;45:1567–73. https://ieeexplore.ieee.org/document/7936592/. [10] Jia YJ, Rajashekara K. An induction generator-based AC/DC hybrid electric power generation system for more electric aircraft. IEEE Trans Ind Appl 2017;53. 2485-94 https://ieeexplore.ieee.org/document/7812661/. [11] Overboom TT, Smeets JPC, Jansen JW, Lomonova E. Decoupled control of thrust and normal force in a double-layer single-sided linear induction motor. Mechatronics 2012;23:213–21. https://doi.org/10.1016/j.mechatronics.2012.06.005. [12] Park GJ, Son B, Seo SH, Lee JH, Kim YJ, Jung SY. Compensation strategy of the numerical analysis in frequency domain on induction motor considering
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Wenqi Lu received the B.S.degree from Zhejiang Ocean University, Zhejiang, China, in 2005, and the Ph.D.degree from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2011. In 2011, he was an Assistant Professor with the Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, where he has been an Associate Professor since 2017. From 2014 to 2017, he was a Post-Doctoral Researcher with the Department of Electrical Engineering, Zhejiang University. From 2017 to 2018, he was a guest researcher with the Department of Energy Technology, Aalborg University. His current research interests include the control of many motors, such as induction motor, brushless DC motor, and permanent magnet synchronous motor, and its application in the robot and high-end equipment.
Mechatronics 62 (2019) 102249 Di Wu received the B.S.degree from Zhejiang Sci-Tech University, Zhejiang, China, in 2017. He is currently pursuing a master degree in control science and engineering, Zhejiang Sci-Tech University, Hangzhou, China. His research interests include the design of the robot motor driver, permanent magnet AC servo system.
Yansuo Zhou received the B.S. degree in Mechanical Manufacture and Automation from Henan Polytechnic University, Henan, China, in 2006, and the M.S. degree from Hong Kong Polytechnic University, Hong Kong, China, in 2016. He is currently pursuing a Ph.D. degree in advanced manufacturing from Tianjin University. From 2006 to 2010, he was an engineer worked in Hangzhou Advance Gearbox Group Co., Ltd. Hangzhou, China. From 2011 to 2017, he was a senior researcher worked in Zhejiang Electrical Group Co., Ltd. His research interests include the automatic control technology and control of synchronous reluctance and permanent magnet machines.
Kaiyuan Lu (M’11) received the B.S. and M.S. degrees from Zhejiang University, Zhejiang, China, in 1997 and 2000 respectively, and the Ph.D. degree from Aalborg University, Denmark, in 2005, all in electrical engineering. In 2005, he became an Assistance Professor with the Department of Energy Technology, Aalborg University, where he has been an Associate Professor since 2008. His research interests include the design of permanent magnet machines, finite element method analysis, and control of permanent magnet machines.
Dong Wang (S’13-M’16) received the B.S. degree from Zhejiang University, Zhejiang, China, in 2004, and the M.S. and Ph.D. degrees from Aalborg University, Denmark, in 2006 and 2016, respectively, all in electrical engineering. From 2006 to 2012, he was with Grundfos R&D China, Suzhou, China, as a Senior Motor Engineer, working on the design and analysis of the permanent magnet machine and devices. From 2016 to 2017, he was a Postdoc Researcher with the Department of Energy Technology, Aalborg University, where he has been an Assistant Professor since 2017. His research interests include design and control of synchronous reluctance and permanent magnet machines.
Hao Wu is currently pursuing the B.S.degree at Mechanical and Electronic Engineering, Zhejiang Sci-Tech University, Hangzhou, China. His research interests include the control of the asynchronous motor and its application in the robot and high-end equipment.
Liangliang Yang received the B.S. degree in vehicle engineering from Chongqing University of Technology, Chongqing, China, in 2001, and the M.S. and Ph.D. degrees in Mechatronic Engineering from Huazhong University of Science & Technology, Wuhan, in 2005 and 2009, respectively. From 2009 to 2015, he worked as a Lecture in Zhejiang Sci-Tech University, Hangzhou, China. From 2015 to Now as an Associate professor. Principally on the design and analysis of the high speed and high precision motion control system. His research interests include the design and control of high-speed and high-precision motion control system.