Information processing for mechatronic systems

Robotics and Autonomous Systems ELSEVIER

Robotics and Autonomous Systems 19 (1996) 117-134

Information processing for mechatronic systems Rolf Isermann 1 Institute of Automatic Control, Laboratory for Control Engineering and Process Automation, Technical University of Darmstadt, Landgraf-Georg-Str. 4, D-64283 Darmstadt, Germany

Abstract

The integration of mechanical systems and microelectronics opens many new possibilities for process design and automatic functions. After discus sing the mutual interrelations between mechanical and electronic design the different ways of integration within mechatronic systems and the resulting properties are described. The information processing can be organized in multilevels, ranging from low-level control, through supervision to general process management. In connection with knowledge bases and inference mechanisms intelligent control systems result. The design of control systems for mechanical systems is described, from modeling, identification to adaptive control for nonlinear systems. This is followed by solving supervision tasks with fault diagnosis. Then design tools for mechatronic systems are considered and examples of applications are given, like intelligent control of an electromechanical throttle actuator and force and torque reconstruction for a robot. Keywords: Mechatronic systems; Intelligent control; Nonlinear control; Adaptive control; Supervision; Fault diagnosis; Hardware-in-

the-loop simulation; Intelligent automobile actuators; Torque reconstruction; Robot control

1. Integration of m e c h a n i c a l and electronic systems

Mechanical systems are increasingly integrated with actuators, sensors and electronics. Besides the basic energy flow in the mechanical system an information flow in the electronic system enables a variety of automatic fUnctions. This leads to m~chatronic systems which consist of: - mechanics (mechanical engineering, precision mechanics) and coupled processes (e.g. thermal, electrical). - e l e c t r o n i c s (microelectronics, power electronics, measurement and actuator technology),

1 E-mail: isermann@~:l.rte-technik.th-darmtadt.de.

- information technology (systems theory, automa-

tion, communication, software design, artificial intelligence), see e.g. [5,13,14,20,27,37] Fig. 1. The design of the functions within mechatronic systems is performed as well on the mechanical as on the digital electronic side. Herewith the mutual interrelations play an important role and the creation of synergetic effects. Mechatronic systems are developed for mechanical elements, machines and vehicles, and precision mechanics. Examples are: (a) Mechanical elements with integrated electronics - suspension systems, - vibration dampers, - clutches, elastic or with friction, - bearings, mechanical or magnetic, - gears, mechanical (tooth-, chain-, belt-gears).

0921-8890/96/$15.00 © 1996 Elsevier Science B.V. All rights reserved PII S0921-8890(96)00040-1

R. Isermann/Robotics and Autonomous Systems 19 (1996) 117-134

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Fig. 1. Integrationof mechanics, electronics, and information technologyleads to mechatronic systems.

Table 1 shows five important development steps for mechatronic systems, starting from a purely mechanical system and resulting in a fully integrated mechatronic system. Depending on the kind of the mechanical system the intensity of the single development steps is different. For precision mechanical devices already fairly integrated mechatronic systems do exist. The influence of the electronics on mechanical elements may be considerable, as shown by adaptive dampers, anti-blocking system brakes and automatic gears. However, complete machines and vehicles show first a mechatronic design of their elements and then slowly a redesign of parts of the overall structure as can be observed in the development of machine tools, robots and vehicle bodies. Table 2 indicates some properties of conventional and mechatronic systems and the advantages gained by the integration, and Table 3 some examples for mechatronic systems for cars.

2.

Information

mechatronic

processing

structures

for

systems

(b) Machines with integrated electronics - p o w e r producing .machines like electrical drives, pneumatic and hydraulic drives, water-, steam-, gas-turbines, combustions engines, etc., - p o w e r consuming machines like generators, pumps, compressors, machine tools, robots, printing machines, etc., - vehicles, like automobiles, ships, aircraft.

Governing mechanical systems by actuators results generally via changes of input variables like e.g. positions, speeds, flows, forces or torques, voltages. The directly measurable output quantities are usually positions, speeds, accelerations or forces and currents.

(c) Precision mechanics with integrated electronics - telecommunication devices, - consumer electronics, - data processing devices, - sensors and actuators, - optical and medical devices, Beginning with a classical mechanical-electrical system which results from addingavailable sensors and actuators to the mechanical components one can mainly distinguish two kinds of integration for mechatronic systems [20]: (i) integration of components (hardware integration), (ii) integration by information processing (software integration).

The information processing of direct measurable input and output signals can be organized in several

2.1. Multi-level control systems

levels, compare Fig. 2: - level 1: low-level control (feedforward, feedback for damping, stabilization, linearization), - level 2: high-level control (advanced feedback control strategies), - level 3: supervision incl. fault diagnosis, - level 4: optimization, coordination (or processes), - level 5: general process management. Recent approaches for mechatronic systems mostly use signal processing in the lower levels, for example damping or control of motions or simple superversion. The digital information processing, however,

R. lsermann/Robotics and Autonomous Systems 19 (1996) 117-134

119

Table 1 Steps in the design of mechatronic systems Precision mechanics

Mechanical elements

Machines

Sensors, actuators, discstorages, cameras

Suspensions, dampers, clutches, gears, brakes

El. drives, combustion engines, mach. tools, robots

Pure mechanical system (1)

Addition of sensors, actuators, microelectronics, control functions

(2)

Integration of components (hardware integration)

(3)

Integration by information processing (software integration)

(4)

Redesign of mechanical system

(5)

Creation of synergetic effects

Fully integrated mechatronic systems Examples

Note: The size of a circle indicates the present intensity of the respective mechatronic development step:

large

0

medium

0 little

Table 2 Properties of conventional and mechatronic designed systems

1 2 3 4 5 6 7 8 9 10

Conventional design

Mechatronic design

Added components

Integration of components (hardware)

Bulky Complex mechanisms Cable problems Connected components

Compact Simple mechanisms Bus or wireless communication Autonomous units

Simple control

Integration by information processing (software)

Stiff construction Feedforward control linear (analog) control Precision through narrow tolerances Nonmeasurable quantities change arbitrarily Simple monitoring Fixed abilities

Elastic construction with electronic damping Programmable feedback control (nonlinear) digital control Precision through measurement and feedback-control Control of nonmeasurable estimated quantities Supervision with fault diagnosis Learning abilities

allows the solutions of much more tasks, like adaptive control, learning control, supervision with fault diagnosis, decisions for maintenance or even redundancy actions, economic optimization and coordination. The tasks of the higher levels are sometimes summarized as "process management".

2.2. Special signal processing The described methods are partially also applicable for nonmeasurable quantities which are reconstructed by using mathematical process models. By this way it is possible to control e.g. damping ratios, material and heat stress, and slip or to supervise quantities like

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R. lsermann/Robotics and Autonomous Systems 19 (1996) 117-134

Table 3 Realization examples of mechatronic systems in cars for the properties given in Table 2. Conventional design

Mechatronic design Integration components (hardware) Electronic injection High-pressure pump and magnetic injection valves

5 6 7 8

Added components Mechanicalduplex carburator mechanicalcontrolled injection pump with rotating piston Many cables Belt driven auxiliaries Simple control Stiff drive chain mechanicalgas pedal Feedforwardcontrolled actuators Manualsteering of cars during spinning

10

Monitoring of exhaust gases through maintenance inspection Fixed programs for automatic gear

1 2 3 4

.......................... I infe.... strategies

I qualitative

2.3. Model based and adaptive systems

: I i~i~ - mll~g~nt decisions

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Adaption of automatic gear to individual driver determine derivated or integrated quantities, or state variable ooservers.

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Bus cable Decentralized driven auxiliaries Integration by information processing (software) Elastic drive chain with algorithmic damping through engine control Electronic nonlinear throttle control Feedback controlled actuators with friction compensation Feedback control of slip angle by state observer and differential braking On-board misfire detection by speed measurement of crankshaft

past ( mc'mo~ ), prediction

I iI

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Fig. 2. Advanced intelligent automatic system with multi-control levels, knowledge base, inference mechanism and interfaces. resistancies, capacitances, temperatures within components, or parameters of wear and contamination. This signal processing may require special filters to determine amplitudes or frequencies of vibrations, to

The information processing is, at least in the lower levels, performed by simple algorithms or software modules under real-time conditions. These algorithms contain free adjustable parameters, which have to be adapted to the static and dynamic behavior of the process. In contrast to manual tuning by trial and error the use of mathematical models allows precise and fast automatic adaptation. The mathematical models can be obtained by identification and parameter estimation which use the measured and sampled input and output signals. These methods are not restricted to linear models, but also allow to identify several classes of nonlinear systems. If the parameter estimation methods are combined with appropriate control algorithm design methods adaptive control systems results, which can be used for precise controller tuning continuously or only for commissioning [22]. This is described in more detail in Section 3. 2.4. Intelligent systems The information processing within mechatronic systems may range between simple control functions and intelligent control, as shown in Fig. 2. An intelligent control system is organized as an on-line expert system and comprises:

R. Isermann/Robotics and Autonomous Systems 19 (1996) 117-134

multi-control functions (executive functions), knowledge base, - inference mechani~sms, - communication interfaces. The on-line control functions are usually organized in multi-levels, as already described. The knowledge base contains quantitative and qualitative knowledge. The quantitative part operates with analytic (mathematical) process models, parameter and state estimation methods, analytic design methods (e.g. for control and fault detection), and quantitative optimization methods. Similar modules hold for the qualitative knowledge, e.g. in form of rules (fuzzy and soft computing). Further knowledge is the past history in the memory and the possibility to predict the behavior. Finally tasks or schedules must be known. The inference mechanism draws conclusions either by quantitative reasoning (e.g. Boolean methods) or by qualitative reasoning (e.g. possibilistic methods) and takes decisions for the executive functions. Finally communication between the different modules, an information management data base and the man-machine interaction, has to be organized. Based on these functions of an on-line expert system an intelligent system can be built up, with the ability "to model, reason and learn the process and its automatic functions within a given frame and to govern it towards a certain goal". Hence, intelligent mechatronic system~,; can be developed, ranging from "low-degree intelligent" [15], as intelligent actuators, to "fairly intelligent systems", as e.g. self-navigating automatic guided vehicles. An intelligent mechatronic system e.g. adapts the controller to the mostly nonlinear behavior (adaptation) and stores its controller parameters in dependence on the position and load (learning), supervises all relevant elements and performs a fault diagnosis (supervision) to request for maintenance or if a failure occurs to fail safe (decisions on actions). In the case of multiple components supervision may help to switch off the faulty component and to perform a reconfiguration of the controlled process. -

2.5. Electronic systems

The increasing integration density of microelectronics leads to improving solutions with regard to costs per calculations, larger mechanical robustness and less

121

space requirements. Because of the fast mechanical process behavior relatively high sampling rates in the range of 5 Hz
3.

Advanced

control

methods

for

mechanical

systems

Because of the integration of various functions the use of modem tools plays an important rule for the design of the control system if higher performances are required. It is proposed to consider the basic control as a knowledge based multi-level feedback control system which is shown in Fig. 3. It is a part of the intelligent system of Fig. 2. The knowledge base consists of mathematical process models, parameter estimation and controller design methods and control performance criteria. The feedback control is organized in lower-level and higherlevel controllers, a reference value generation module and controller parameter adaptation. With this structure the main control functions of mechatronic systems can be organized. In this section some functions will be considered briefly.

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Fig. 3. Knowledge based multi-level feedback control for mechatronic systems. 3.1. Mathematical process models A precondition for precisely adapted algorithms for damping and control is the knowledge of mathematical process models for the static and dynamic behavior. For mechanical processes it can be recommended to obtain the model structure by theoretically modeling and the parameters by parameter estimation. For machines, consisting e.g. of a motor, drive chain and a working process the models show frequently following properties [15]: - The drive chain elements are linearizable. Bearings can be linearized if the motion is unidirectional. For bidirectional motions the Coulomb friction generates a hysteresis, resulting in nonlinear models. However, it is possible to use direction dependent linear models. Motors and working processes have often nonlinear behavior. Some parameters (e.g. mass, stiffness) are known, others (e.g. damping or friction coefficients, load parameters) are not known and change with time. - Model orders n may range within 3 < n < 20. Hence, software tools for modeling and computer algebra are recommended [36], and model reduction techniques are required. -

with recursive estimation algorithms based on leastsquares methods allows to track time varying parameters of discrete-time and continuous-time models for linear and certain types of nonlinear processes. Suitable nonlinear models for mechatronic systems are for example nonlinear static polynomials or hysteresis curves with linear dynamics or nonlinear dynamics with linearity in the parameters. With known parameters state estimation provides information on internal variables which cannot be measured directly. Discretetime models have the advantage to be better suited for the various algorithms. Continuous-time models should be performed if physical defined quantities (parameters, states) are essential. Parameter and state estimation methods are the basis for adaptive control or adaptive damping or for model based fault detection. 3.3. Lower-level feedback control The goal of the lower-level feedback is to linearize the system, to provide a certain dynamic behavior (e.g. enforcement of damping), to compensate for nonlinearities like friction, and to reduce parameter sensitivity. Some examples are as follows. 3.3.1. Damping of high-frequent oscillations Weakly damped higher-frequent oscillations appear e.g. in multi-mass drive chains. The damping can generally be improved by high-pass filtering the outputs and using a state variable feedback or PD (proportional derivative) feedback [18].

3.2. Parameter and state estimation

3.3.2. Compensation of nonlinear static characteristics Nonlinear static characteristics are present in many subsystems of mechanical processes. Frequently a first nonlinearity appears in the force or torque generating part like an electromagnet or a pneumatic or hydraulic actuator where e.g. the force Fu = f ( U * ) , Fig. 4, follows a nonlinear static characteristic. This nonlinearity can now be compensated by an inverse characteristic U* = f - l ( u ) such that I/O-behavior Fu = f ( U ) becomes approximately linear [23].

The causal relations between measured input and output signals can be used to obtain information on the internal process behavior. Parameter estimation

3.3.3. Friction compensation For many mechanical systems the friction can be approximately by

-

-

R. Isermann/Robotics and Autonomous Systems 19 (1996) 117-134 level

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process parmneter estimation & ~ll=r design & supervision

higher Icv©l

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However, if the amplitude is too large, the control performance deteriorates. Direct friction compensation. This approach is from the theoretical point of view the ideal control strategy for friction compensation. By adding the compensation value

WFC(t) = --fvc± sign I;'(t),

(2)

or if Y(t) is not available, ........................................

~;,,,g~i~ L~;~,;&;

Fig. 4. Typicaladaptivecontrol schemefor a mechanicalprocess with control of position Y: GR1 - Force generation control with compensationof static nonlinearity(minorcontroller);GR2 - Position controller of the mechanical process with friction compensation (major controller).

FF±('t) = fFC± sign I;'(t) + fFv± }:'(t), lI?(t)[ > 0,

(1)

where fFC is the Coulomb friction and fFv the linear viscous friction coefficient which may be dependent on the motion direct:ion, indicated by + or - . Especially the Coulomb fiiction has a strongly negative effect on the control performance, if a high-positioning accuracy is required, because it leads to a hysteresis effect. When the process stops within the hysteresis width before the set point is reached, only the integral part of the position controller can compensate for the offset. This yields a significant loss of control performance and accuracy, especially during small position changes. The basic idea of :friction compensation is to compensate the relay function of the Coulomb friction by adding an adequate compensation voltage Ucomp to the normal control action U. Different methods such as dithering, feedforNard compensation and adaptive friction compensation are alternatives [ 15,23]. Dithering. Dynamic linearization or so-called "dithering" is the classical way of analog and even digital friction compensation. By adding a highfrequency, periodic signal to the control action U, the friction is compensated during half the period, whereas during the :second half friction it is undercompensated. The method is quite robust with regard to the amplitude and frequency of the dither signal.

WFC(t) =--fFC± sign ew2(t)

(3)

to the reference value W1 of controller GR1 or directly to the controller output U, an inverse function of the Coulomb relay characteristic is obtained, compare Fig. 4. Adaptive friction compensation. In the preceding methods, the friction compensation was realized by a feedforward control strategy. Better results may be expected, if the actual friction value can be adapted in an additional feedback friction control loop. Therefore an adaptive friction compensation was developed which interprets the deviation between the measured output Y(k) and a linear reference model Y,n(k) as friction effect [29].

3.4. Higher-level feedback control The task of the higher-level controller is to generate a good overall dynamic behavior with regard to the servo dynamics function due to changes of the position reference W1 (t) and to compensate for disturbances stemming e.g. from variations of the load forces FL (t), see Fig. 4. This high-level controller may be realized as a parameter optimized controller of PID-type or a state controller with or without state observer. A state observer is required if only the position Y(t) is measurable. If both Y(t) and l~(t) can be measured, Y(t) can be obtained by differentiation of l;'(t) (if required at all) such that no state observer is needed, see [21]. Note that the control scheme of Fig. 4 forms a cascaded control scheme, with GR1 as the minor controller and the friction compensation, both as a lower-level feedback from the mechanical process. The controller GR2 is then the major controller, generating the reference value Wl(t) for the minor controller.

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R. lsermann /Robotics and Autonomous Systems 19 (1996) 117-134

The control scheme may be expanded by additional feedback controllers from a load or working process which is coupled with the mechanical process shown in Fig. 4 such resulting in a multiple cascaded control system.

3.5. Adaptive control During normal operation, most of the processes change their parameters in a determined way. An improved control performance over the whole range of operation as well as the lifetime may be obtained by adaptive control. Parameter scheduling. Parameter scheduling based on the measurement of varying operation conditions is an effective method to deal with known and approximately time-invariant process nonlinearities. Supposing measurable auxiliary variables V, that correlate well with the process changes, the adaptation of the controller parameters/" is performed as functions of V (parameter schedule). Parameter adaptive control systems. Parameter adaptive control systems are characterized by using identification methods for parametric process models. This is indicated in the adaptation level of Fig. 3. Digital adaptive control works well if the assumptions for their design and convergence are satisfied. This includes proper excitation of the process dynamics as well as the appropriate model structure. For the cases where the assumptions are violated a supervision level is required which takes appropriate actions [22]. Parameter estimation has proven to be an appropriate basis for the adaptive control of mechanical processes, as indicated in Fig. 2, including the adaptation to nonlinear characteristic, Coulomb friction, and the unknown parameters like masses, stiffness, damping, see [15,22,23,29].

3.6. Fuzzy control The development of fuzzy logic theory [40] stimulated alternative ways to solve automatic control problems. Based on these ideas fuzzy controllers were proposed [28] which describe human control in linguistic form. As fuzzy logic provides a systematic framework to treat vague variables and knowledge it should be applied primarily if sensors yield imprecise outputs, the process behavior is only qualitatively

known or the automation functions cannot be described by equations or Boolean logic. As discussed in [19] the potentials of fuzzy logic approaches in general increase with higher-automation levels, because the degree of the qualitative knowledge and the required intelligence in general grow with the hierarchical level. The static and dynamic behavior of most mechanical systems can be rather precisely described by mathematical process models obtained through theoretical modeling and identification methods. Hence, there is in many cases no need to apply fuzzy concepts for the control of mechanical systems in the lower levels. However, fuzzy control concepts may be of interest for: - fuzzy tuning and adaptation of classical controllers, - fuzzy quality and comfort control, - fuzzy control for special (abnormal) operating conditions. Especially for the reference value generation of underlying (classical) control systems, Fig. 3, where the quality or comfort and therefore the human reception plays a role, fuzzy rule based methods offer interesting possibilities, i.e. for the higher-control levels. Examples for such mechatronic systems are: - the comfort control of suspensions in passenger CarS,

the comfort of start-up of automobiles with clutch manipulation and gear shifting, - distance and velocity control of automobiles and elevators. For a more detailed description see [19] -

4.

Supervision

and

fault

detection

An important feature of an intelligent system is the automatic supervision and fault diagnosis of its components. Fig. 5 shows a component influenced by faults. External faults are for example caused by the power supply, contamination or collision, internal faults by wear, missing lubrication, actuator or sensor faults. If the faults influence directly measurable output variables, they may be detected by an appropriate signal evaluation. The corresponding functions are called monitoring, if the measured variables are checked with regard to a certain tolerance of the normal values and

R. Isermann/Robotics and Autonomous Systems 19 (1996) 117-134

internal

extemal faults t,i

U

_

.1

process

on parameter estimation or parity equations. For fault detection with state estimation and also parity equations discrete-time models can be used. Advanced supervision and fault diagnosis is a basis for: - improving reliability and safety, - state dependence maintenance, - triggering of redundancies and reconfiguration.

5.

_Oo+AOo _Xo+A_Xo Fig. 5. Scheme of a component (process) influenced by faults. alarms are triggered if the tolerances are exceeded. In the cases where the limit value violation signifies a dangerous state an appropriate action can be indicated automatically. This is called automatic protection. The classical ways of limit value checking of some few important measurable variables are appropriate for overall supervision. However, developing component faults are only detected at a rather late state and the available intormation does not allow an indepth fault diagnosis. Research efforts have shown that the use of process models allows an early fault detection in connection with normal measured variables. [16,17]. Then nonmeasurable quantities like state variables and parameters may be estimated. With this improved knowledge a supervision with fault diagnosis becomes possible. Figure 6 shows the scheme of a model-based fault detection and diagnosis. The fault detection includes parameter or state estimation or parity equations and generates symptoms as changes from the normal behavior. In addition heuristic symptoms observed by the operator like noise, vibrations, smell or based on the process history like last maintenance or repair, running time can be taken into account. On the basis of a unified symptom representation, e.g. in the form of fuzzy sets, an inference mechanism then performs the fault diagnosis. For this fault symptom causalities have to be established and approximate reasoning stategies may be applied. For more details see [17]. A considerable aclvantage, if the same process model can be used fc,r both, the (adaptive) controller design and the fault detection. In general continuous time models are preferred, if fault detection is based

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Sensors

and

actuators

5.1. Sensors The required measurement variables for mechatronic systems are electrical quantities like position, speed, acceleration, force, torque, pressure or thermal quantities like temperature or heat flow. A survey of known measurement principles is e.g. given in [6,8,30,34,38]. Of special importance for mechatronic systems are the integration with the process, the dynamics, the resolution, mechanical and thermal robustness, wear resistancy, toucbless transfer, miniaturization and easy transfer to digital signal processing. Therefore incremental sensors f o r positions or speeds show an increasing trend. The integration of the sensor and the signal processing on a common carrier becomes more and more important. This integration, possibly on one chip, has the following advantages: less cost and space, higher precision, less disturbances through digital transmission. The digital processing of sensor signals allows the programming of noise filtering, linearization of nonlinear characteristics, correction of cross sensitivities or hysteresis, self-calibration, compensation of dynamic lags and of drift [29]. Hence, a development can be observed from a classical measurement chain to a smart sensor component. A drawback of sensors and microelectronics based on silicium technology is the limited temperature range until about 80-100°C. Gallium arsenide (GaAs) technology may allow to lift the range until 300400°C [11]. 5.2. Acma~rs Actuators in mechatronic systems transform electrical inputs into mechanical outputs such as position,

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R. lsermann/Robotics and Autonomous Systems 19 (1996) 117-134

~ FAULTS observed ANALYTICAL SYMPTOM GENERATION

ANALYTICAL KNOWLEDGE ANALYTICAL PROCESS MODEL

~

CHANGE DETECTION

O ~S IL OBSERVATIONS

[

PROCESSING _J_ characteristic

L

CHANGE DETECHON

WEIGtrI~G ~¢1~ '~I~1~,

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FAULT- / ~ DIAGNOSIS

(pattern-based)

causer,s

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FAULTSYMPTOM

PROCF.~SSTATISTICS

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NORMAL BEHAVIOUR

PROCESSHISTORY

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MODI~

FEATURE EXTRACTION

~'rRACrlON I

I HEURISTIC PROCESS

[q

• values

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HEURISTIC KNOWLEDGE

/

DATA-

ESTIMATION

HEURISTIC I SYMFrOM GENERATION

~F

FORWARD/ ~ BACKWARD CHAINING

FAULT DECISION

[L

diagnosed faults

r~e-l~m3

Fig. 6. Scheme of knowledge-based fault detection and diagnosis.

angle, force or torque. The supporting energy is usually electrical, pneumatic or hydraulic. The actuator principles can be classified into three major groups: -electromagnetic actuators (e.g. electrical motors (DC, AC)), step motors, electromagnet), - fluidic actuators (hydraulic, pneumatic), - unconventional actuators (e.g. piecoelectric, magnetostrictive, memory metal). An evaluation of typical application areas, control properties and robustness is given in [35]. If a high precision of positioning and fast dynamics are required, generally a position control loop is necessary. However, the following properties hinder a precise control: friction and backlash (hysteresis effects), nonlinear static characteristics, operating point and wear dependent parameters. It was shown in [23] how these disadvantages can be compensated by a

model based digital position control. Compensation of nonlinear characteristics and friction compensation with parameter adaptive control showed a considerable improvement of control performance for an electromagnetic and pneumatic actuator [25].

6. Design tools The integration by information processing for highperformance mechatronic systems requires that the static and dynamic behavior of all components is well designed. This means a systematic development, starting with modeling and simulation and using methods for identification and computer aided control system design. Of special importance for the mechatronics design seems to be the consequent use of software tools.

R. lsermann/Robotics and Autonomous Systems 19 (1996) 117-134

The computer aided development of mechatronic systems usually comprises: (a) constructive specification in the engineering development stage using CAD and CAE-tools, (b) model building for obtaining static and dynamic process models, (c) transformation into computer codes for system simulation, (d) programming and implementation of the final mechatronic software. Some software tools are described for example in [31]. A broad range of CAD/CAE tools is available for 2D and 3D-mechanical design such as Auto CAD with a direct link to CAM (computer aided manufacturing); PADS for multi-layer printed-circuit board layout, etc. However, the efforts for computer aided modeling are comparable low. Programs for modeling and simulation of mechanical multi-body systems are NEWEUL [26], ADAMS, DADS, MECHANICA, SIMPACK, see e.g. [12]. They are suitable for standards tasks of engineering mechanics. Development steps for modeling and simulation of mechatronic systems are taken into account by MOBILE [12]. A program system for computer aided modeling by using computer algebra was developed in [36]. An object-oriented language Dymola for modeling of large combined systems is described in [7,31]. These packages are based on specified ordinary differential equations, algebraic ,equations and discountinuities. For system simula~ion (and controller design) a variety of program systems exist like ACSL, SIMPACK, MATLAB/SIMULINK, MATRIX-X. These simulation techniques are valuable tools for the design, as they allow to study the interaction of components, and the variations of design parameters before manufacturing. However, they are in general not suitable for real-time simulation. A recent description of the state of computer-aided control system design can be found in [24]. A systematic design for linear multi-variable systems is performed by ANDECS [9]. This is a modular designed software, comprising modeling, simulation and control design by combining several packages in an object-oriented way. For the further dew,qopment of the final mechatronic system the hardwan,-in-the-loop simulation plays a special role. Various kinds can be realized:

127

(a) Simulation of electronics. Actuators, mechanics and sensors are real hardware. Information processing is realized in e.g. a process computer for development and also for testing, in case of malfunctions at the side of microcomputer, power supply, cable connections, etc. (b) Simulation of mechanics, actuators and sensors. Microcomputer hardware and software is real. Real-time simulation of the remaining system requires appropriate mathematical models such that variations on the mechanical side e.g. different realizations of the process and actuators can be investigated. (c) Simulation of mechanics. Microcomputer hardware and software, actuators and sensors are real. Real-time simulation of the mechanics allows testing of the electronic components and effects in case of their malfunctions. For the reai-time simulation of mechanics, sensors and actuators, transputers and signal processors are suitable, e.g. [10].

7. Application examples In the following, two examples of research projects are shown where the goal was to realize some ideas of mechatronic principles. 7.1. Nonlinear adaptive control and fault detection for an automobile actuator

Automobile actuators have to operate very reliable under hard ambient conditions such as a wide temperature range, vibrations and disturbances in signals and power supply. Friction and time varying process parameters, which are mainly caused by temperature influences, make it difficult to fulfill fast and precise positioning with conventional linear control algorithms. The given throttle valve actuator is used in an ignition combustion engine to control the air mass flow through the intake manifold into the cylinders. This automotive actuator is embedded in various control systems such as traction control and velocity control which require fast and precise operation. Figure 7 shows the mechanical function of the actuator. A permanently excited DC motor with a gear turns the throttle value against the torque of a spring.

128

R. lsermann/Robotics and Autonomous Systems 19 (1996) 117-134

electrical ed s

ed

voltage)

commutator

permanently excited d.c. motor

Fig. 7. Scheme of the investigated electrical throttle valve actuator.

The controller output is a pulse width modulated (PWM) armature voltage UA. Adaptive control. Theoretical modeling and parameter estimation has shown that the process behavior can be described by two linear differential equations with the physical process coefficients. p = [RA, ~P, c0e, J, CF, MRI, M0] T

1

0

0

~

(4)

with, RA the armature resistance, qJ the magnetic flux linkage, c0e the DC value of the electromagnetical subsystem, J the total moment of intertia, MR1 the viscous friction coefficient, CF, the spring constant and M0 the DC value of the mechanical subsystem [33]. The developed control algorithm consists of a linear PIDT1 controller with an additional friction compensator. The parameters of the linear part of the controller are tuned automatically and on-line by evaluation of a loss function and minimization with the downhill-simplex algorithm, while the nonlinear friction compensation is based on parameter estimation. The obtained control performance is shown in Fig. 8. It satisfies the requirements for dynamic and precise positioning over the whole operation range. Fault detection. Based on the same process model as for controller design, a model based fault detection and diagnosis was developed, using only the voltage, the current and the throttle position as measurements. The deviations of the physical coefficients of the actuator from their normal values P0 are used as analytical

0 0

iiiJ 0.5

15

2

25

32

~

time [sec]

Fig. 8. Control results with PID-T1-controller and friction compensation.

symptoms for fault detection. These parameters are determined by a recursive parameter estimation. Fourteen different faults have been generated in the real actuator. The obtained patterns of changes are shown in Table 4. All faults cause a unique symptom pattern. Additionally a set of four structured discrete time parity equations was developed, where each parity equation is decoupled from another process input and output variable, respectively (rl: [~oK],r2: [IA], r3: [UA], r4: [M0]). The application

R. lsermann/Robotics and Autonomous Systems 19 (1996) 117-134

129

Table 4 Process parameter deviations and parity equation residuals for different actuator faults (0 --+ no significant change, + ~ increase, + + --~ large increase, -- --~ decrease, - - --~ large decrease Features

Faults

Parameter estimation kV

C0e

J

CF

MR!

M0

r!

r2

r3

Incr. spring p r e t e n s i o n

0

0

0

0

0

0

--

+

-

+

-

Decr. spring pretension Commutator shortcut Arm. winding shortcut Arm. winding break Add. serial resistance Add. parallel resistance Increased gear friction Offset fault UA Offset fault IA

0 0 . . . . 0 -++ ++ 0 . . . . 0 0 0 0 0 0

0 0 0 0 0 0 0 + --

0 + + 0 0 0 + 0 0

0 + + + 0 + + 0 0

0 ++ ++ ++ 0 ++ ++ 0 0

++ 0 0 + 0 0 0 0 +

0 + + + + -+

+ 0 + -0

+ + + + + + 0 +

0 + + + --

Offset fault tpK

0

0

0

0

0

0

--

0

+

--

0

Scale fault UA Scale fault IA Scale fault ~K

+ -0

+ 0 --

+ 0 0

+ + .

+ +

+ + .

+ +

+ 0

-0 +

0 + --

+ -0

leads to the residual pattern also shown in Table 4. The residual changes; show a direct connection to the offset and scale faults in the sensors of UA, IA and ~0K but no unique relations to the actuator faults. Hence, the parity equations can only be used for fast detection of some o f the fauks, but with little computational effort. Fault diagnosis. For fault diagnosis the reliable information of symptom generation by parameter estimation is used. Fourteen parallel neuro-fuzzy networks were implemented and each o f them is made sensitive to one fault [32]. The network consists of an antecedent layer, a relation layer and a conclusion layer which models fuzzy reasoning. The relations of the causal net are identified with adjustable A N D O R connectives of the symptoms, [3]. The subsets of the input variables are automatically determined, the most significant rules are extracted and finally the extracted rule base is optimized. This application example has shown that the integration of different methods leads to a rather complete fault detection and d]:agnosis by fast fault detection with little computational effort by parity equation,.z, - fault detection with detailed and deeper information by parameter estinmtion, fault diagnosis by an adaptive neurofuzzy reasoning [33]. -

-

Parity equation

RA

.

.

r4

7.2. Adaptive external torque observation in robotic joints by measuring motor signals Feasible application of robot manipulators to complex manufacturing and handling tasks, in which the robot's endeffector is compliantly interacting with the environment, requires the precise control and supervision of the interaction forces in the face of environmental uncertainties and variations. Actually, such control can be achieved by equipping manipulators with force sensing devices mounted either at the robot's wrist or on each joint. However, these sensors are both, sensitive to failures and expensive. Thus, the functional replacement of the sensor components by an integrated mechatronics system with information processing of available signals in higher levels and accurate process knowledge is desirable. An alternative method for obtaining force information is based on current measurements o f the actuating drive utilizing the effect of a significant increase of the actuator current as a reaction to the external force applied to a stiff position controlled robot joint, see [2,4]. The external force/torque estimation methods are solely utilizing the drive signals available in conventional robot control systems which in case of a brush type DC servomotor driven robot axis include the armature current IA, the motor velocity oJ and the joing angle q, as depicted in Fig. 9. The armature cur-

R. Isermann/Robotics and Autonomous Systems 19 (1996) 117-134

130

C•xT ;-

\

\m /

. . . . . . . .

the manipulator axes. Note that particularly the static terms significantly contribute to the achievable accuracy of external force estimation. The gravitational torque MG solely depends on the actual joint configuration and can analytically be described by the superposition of trigonometric terms. The vector of disturbance torques MEXT is related to the vector of generalized external forces fCxT referenced to an arbitrary cartesian coordinate system C by

axis 3

ax,,

",,

|Il incremental cz

MEXT = NTCj T (q) CfEXT,

1: t "l-n m

encoder

!

axis 1

',

/

I

I

\

Fig. 9. Schemeof the drive chain of a single robot axis indicating the required measurement quantities 1A, o) and q.

D(

I

q

C o u l o m b friction

characteristics

Fig. 10. Nonlinear dynamic model of a single joint drive including a signal model of Coulomb friction. rent of the position-controlled robotic drive system is directly affected by the external torque exerted on the respective joint. The variations of the armature current interpreted as a disturbance torque are taken as the input of an adaptive disturbance observer based on a precise continuous time model of joint drive dynamics. The developed dynamic model, shown in Fig. 10, is a hybrid approach to robot modeling comprising a nonlinear dynamic model of the mechanical subsystem [1] supplemented by a sophisticated signal model of Coulomb friction. In general, the effective moment of inertia JM(q), transformed into the actuator space, varies within a wide range depending on the actual configuration of

(5)

N and J(q) denote the nonsingular transmission matrix and the manipulator Jacobian, respectively. In case of decoupled transmission elements, the matrix N includes n diagonal elements representing the total gear ratio v of each joint drive. The sophisticated friction model, describing the kinetic plus viscous friction characteristics, is motivated by numerous experiments revealing a significant dependency of the Coulomb friction on the actual joint position and the applied external torque. Further experiments supported the existence of harmonic friction components at discrete spatial frequencies with load dependent amplitudes which can be attributed to transmission errors in the multi-stage gear unit. The static friction behavior, however, is characterized by a remarkable repeatability. In essence, the proposed external force/torque reconstruction method, depicted in Fig. 11, is carried out in two sequential steps: A signal based compensation of the nonlinear static (gravitational and frictional) torques ensued by the estimation of the external torque based on an adaptive linear disturbance observer. The nonlinear torque compensation is aimed at rejection of the influence of gravitation and varying Coulomb friction on the measured actuating torque MA. The gravitation torque MG, consisting of trigonometric expressions, can analytically be determined. However, for accurate estimation of the actual static friction torque a signal based approach is proposed. Since the average-mean value Mc0+ of Coulomb friction is exposed to substantial changes depending upon the actual position and the external torque to be estimated, the average-mean Coulomb friction value is determined within a preceding identification phase and used to fill out a three-dimensional lookup-table. Due to the significant dependency of the harmonic friction components on the external torque a real-time analysis

R. lsermann/Robotics and Autonomous Systems 19 (1996) 117-134

131

MEXT 0) It

JOINT DRIVE

II

II

DYNAMICS

--"1

I

I I I I I

^

I •

I I

I

eormlatior,.,~'ll " analy~"

J

FFT

g

I I I I I

li"

IF

A

Mexr It

Fig. 11. Structure of the external torque observer with nonlinear signal based friction compensation. of the armature current is suggested. Thereby, the amplitudes and the phase lags of the discrete spatial frequencies assigned to Coulomb friction are estimated using the fast Fourier transform (FFT)or correlation methods. Compared to the FFT algorithm the orthogonal correlation method for discrete frequencies in its recursive form has proved to be more accurate and less computational time consuming. Based on the dynamic drive model a linear secondorder disturbance observer, see Fig. l l , is designed by pole-placement. The external torque MEx'r is interpreted as a steady state setting the derivative 8MExT/St to zero. The actuating torque MA(t) compensated by the nonlinear static (gravitational and frictional) torques is. taken as observer input, and the motor velocity o9(t) as observer output. The dynamic

model parameters utilized for the design of the observer feedback are obtained by applying standard parameter estimation techniques for continuous-time systems in a pr~-identification phase. In order to account for the dependency of the moment of inertia on the actual joint configuration a gain-scheduled adaptation of the observer dynamics is employed. The necessity of adaptation is checked by a superordinate adaptation strategy. This ensures a gradual variation of the observer feedback with a much lower rate than the sampling rate of the position control system. Several experiments for external force observation have been conducted on an industrial robot of type JH-R-106 equipped by a six d.o.f, force/torque sensor mounted on the robot's wrist. The sensor has a moderate force/torque resolution of 2.0 N and 0.015 Nm,

132

R. lserrnann/Robotics and Autonomous Systems 19 (1996) 117-134

a)

m e a s u r e d drive signals 20

- 100

15

1 :~

~o.

-110~

5 ~ ._Z"

t~

.=

0 ~

~ -0.

-s

cr ® -120~

e-

-10

-130

-15

-1.

-=. 0

5

b)

10

15

-20 20

-140

reference/observed t o r q u e 16 14

*) referenced to load side

!

....................................................

12

........................................

10

.....................................................

i

referencl~ torque::......... " .......

'rf .....................................

bbserved ~::..tor~lue. ............

......./ 6 ....... .~dc.ti~n..c.o~pe.ns.at[ot~.........i / 8 ....... observed .terque .................:

E o

without sighal based

::

. .i i

E

i

0

5

10

time [s]

15

20

Fig. 12. (a) Measured drive signals of a robot axis. (b) Observed external torque in comparision with the reference torque (force/torque-sensor). respectively. The sampling interval for data acquisition is chosen to be identical to that of the digital position controller (4 ms). The analogue measurements are prefiltered by anti-aliasing low-pass filters of fourth order to a cutoff frequency of 20 Hz. In an initial series of force experiments the robot was moving into a compliant workpiece repetitively. The measured drive signals of axis 6 utilized for the estimation of the external torque exerted on this axis are depicted in Fig. 12(a). The comparison of the estimated torque with the reference torque of the force/torque sensor in Fig. 12(b) indicates the accu-

rate and fast reconstruction capabilities of the proposed observation method. The estimation sensitivity can be considered in the order of 0.5 Nm for this particular experimental configuration. The model based observation method, however, is not restricted to external force and impedance control of robot manipulators. It can also be established for the purpose of feedforward disturbance rejection to enhance motion control performance as well as collision detection and damage avoidance. This is an example, where a usually not measurable quantity, the external reaction torque, could be recon-

R. lsermann /Robotics and Autonomous Systems 19 (1996) 117-134

structed b y easy accessible electrical drive signals and d y n a m i c models o f mechanics.

References [1] A. Aboul-E1-Ela, J. BOhm and R. Isermann, Dynamic model approach for force estimation and control of robotic contact tasks, in: Proc. IMACS/IFAC 2nd Int. Syrup. on Mathematical and intelligent Models in System Simulation, Brussels, Belgium (1991). [2] A. Abou-E1-Ela, Adaptive external torque observation of robotic joint drives on friction signal models, in: Proc. IFAC Workshop Motion Control, Munich (1995) 601-608. [3] M. Ayoubi, Fuzzy systems design based on hybrid neural structure and application to the fault diangosis of technical processes, Control Engineering Practice, 4 (1). [4] J. Bthm, Model-based force sensing for an industrial robot by using drive signals, in: IFAC/IFIP/IMACS- Syrup. on Robot control, Vienna, Austria (1991), Preprint. [5] D.A. Bradley, D. Dawson, D. Burd and A.J. Loader, Mechatronics-Electronics in Products and Processes (Chapman and Hall, London 1991). [6] E.O. Dobelein, Measurement Systems: Application and Design (McGraw-Hill, New York, 1983). [7] H. Elmquist, Object-oriented modeling and automatic formula manipulation in Dymola, Scandin. Simul. Society SIMS, Kongsberg (1993) [8] B.A. Gregory, An Introduction to Electrical Instrumentation and Measurement Systems (Macmillan, New York, 1981). [9] G. Griibel Regelungstechnische Entwurfsautomatisierung, Automatisierungtechnik 43 (1995). [10] H. Hanselmann, Hardware in-the-loop simulation as a standard approach for development, customization, and production test. SAE 930207 (1993). [11] H.L. Hartnagel, Semi-conductor properties for high temperature electronics, Materials, Science and Engineering, StraBburg (1994). [12] M. Hiller, Modelling, simulation and control design for large and heavy manipulators, Int. Conf. on Recent Advances in Mechantronics Istanbul, Turkey (1995). [13] IEEE Mechatronics: Designing intelligent machines, in: Proc. IEEE Int. Conf. University of Cambridge (1990). [14] R. Isermann, Ed., Integrierte mechanisch-elektronische Systeme, Fachtal~;ung Tech. Univ. Darmstadt, VDIFortschr. Bet., Reithe 12 (179) (VDI Verlag, Diisseldorf, 1993). [15] R. Isermann, Towards intelligent control of mechanical processes, Control Engineering Practice 1 (2) (1993) 232252. [16] R. Isermann, Fault diagnosis of machines via parameter estimation and knowledge processing, Automatica 29 (4) (1993) 815-835. [17] R. Isermann, Inte~Tation of fault detection and diagnosis methods, in: Proc. IFAC Syrup. SAFEPROCESS. Espoo, Finland (1994).

133

[18] R. Isermann, On the design and control of mechatronic systems IEEE Transactions on Industrial Electronics (special issue on Development in Mechatronics) (1995). [19] R. Isermann, On Fuzzy Logic Applications for Automatic Control, Supervision and Fault Diagnosis (EUFIT, Aachen, 1995). [20] R. Isermann, Modeling and design methology for mechatronic systems, IEEE/ASME Transactions of Mechatronics 1 (1) 16-28. [21] R. Isermann, H. Keller and U. Raab, Intelligent actuators, in: N.N. Gupta and N.K. Sinha, eds. Intelligent Control Systemss (IEEE Press, Piscataway, NJ, 1995) Ch. 21. [22] R. Isermann, K.H. Lachmann and D. Matko, Adaptive Control Systems (Prentice-Hall, London, 1992). [23] Isermann R. and U. Raab, Intelligent actuators-ways to autonomous actuating systems, Automatica 29 (5) (1993) 1315-1131. [24] J. James, E Cellier, G. Pang, J. Gray and S.E. Mattson, The state of computer-aided control system design (CACSD), IEEE Transaction on Control Systems (special issue) (1995) 6-7. [25] H. Keller and R. Isermann, Model-based nonlinear adaptive control of a pneumatic actuator. European Control Conference Groningen (1993). [26] E. Kreutzer and G. Leister, Programmsystem NEWEUL '90. TU Stuttgart, Institut f'tir Mechanik (1991). [27] P.A. McConaill, P. Drews and K.H. Robrock, Mechatronics and Robotics I (IOS Press, Amsterdam, 1991). [28] E.H. Mamdani and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, International Journal Man-Machine Studies 7 (1975) 1-13. [29] C. Maron, Methoden zur Identifikation und Lagerelung mechanischer Prozesse mit Reibung. Dissertation T.H. Darmstadt (VDI Verlag, DUsseldorf, 1991). [30] A.S. Morris, Principles of Measurement and Instrumentation (Prentice-Hall, London, 1988). [31] M. Otter and G. Gruebel, Direct physical modeling and automatic code generation for mechatronics simulation, in: Proc. 2nd Conf. on Mechatronics and Robotics, Duisburg (IMECH, Moers, 1993). [32] T. Pfeufer and M. Ayoubi, Fault diagnosis of electromechanical actuators using a neuro-fuzzy network, 3 Workshop "Fuzzy-Neuro-Systems '95", Gesellschaft f'tir Informatik e.V., Darmstadt, Germany. [33] T. Pfeufer and R. Isermann. Intelligent electro-mechanical servo system, in: Proc. 13th IFAC World Cong. San Francisco, Session 1996 No. 9d-03. [34] E Profos and T. Pfeifer, Eds., Handbuch der indutriellen Mefltechnik. 3. Aufl. (Vulkan Verlag, Diisseldorf, 1992). [35] U. Raab and R. Isermann, Low power actuator principles, VDI/VDE Conf. Actuator 90, Bremen (1990). [36] A. Schumann Beitriige zur rechnergestiitzten Modellbildung dynamischer Prozesse, Dissertation Technische Hochschule Darmstadt (VDI-Verlag, Diisseldorf, 1992) Reihe 8 (304). [37] G. Schweitzer, Mechatronic - a concept with examples in active magnetic bearings, Mechatronics 2(1) (1992) 65-74.

134

R. Isermann /Robotics and Autonomous Systems 19 (1996) 117-134

[38] H.R. Tr'ankler, Taschenbuch der Mefltechnik (Oldenbourg, Miinchen, 1992). [39] H.R. Tr~.nkler, Information processing in sensing devices and microsystems, in: Proc. IFAC Symp. on Intelligent Components and Instruments for Control Applications, Malaga (Pergamon Press, London 1992). [40] L. Zadeh, A rationale for fuzzy control, Journal Dynmamic Systems, Measurement and Control (Series G) 94 (1972) 3-4. Prof. Dr.-Ing.Dr. h.C. Rolf lsermann received both the Dipl.-Ing. degree in mechanical engineering and the Dr.-Ing. degree from the University of Stuttagrt, in 1962 and 1965, respectively. There he became Professor in 1972. Since 1977 he has been Head of the Laboratory of Control Engineering and Process Automation at the Institute of Automatic Control at the Technical University of Darmstadt, Germany. He acted as the chairman of the international program committee of the 10th IFAC-Worldcongress in Munich and of the IFAC-Symposium "Safeprocess" in Baden-Baden, in 1987 and 1991, respectively. In 1989, he was awarded by the Dr. h.c. degree from L'Universit6 libre de Bruxelles. R. Isermann's main area of research interest covers: Proces Modeling, Process Identification, Digital Control Systems, Adaptive Control Systems, Fault Diagnosis and Mechatronic Systems. He also wrote several books and papers on these topics.