- Email: [email protected]
Simulink for process control education
Proceedings of the 9th IFAC Symposium Advances in Control Education The International Federation of Automatic Control Nizhny Novgorod, Russia, June 19-21, 2012
Use of an industrial distributed control system in conjunction with Matlab/Simulink for process control education Rainer Dittmar
West Coast University of Applied Sciences, Heide, Germany (Tel: +49 481 8555-325; e-mail: [email protected]).
Abstract: The paper demonstrates how a four-tank laboratory process equipped with a modern distributed control system (DCS) is used in different control engineering laboratories for the undergraduate and graduate levels. This includes the utilization of commercial tools for DCS engineering as well as the design and commissioning of traditional and advanced control solutions. Matlab/Simulink tools are connected via OPC to extend the laboratory scope with rapid control prototyping and hardware-in-the-loop simulation. Using the same pilot plant, DCS and scientific computing environment in different courses lowers the costs, allows the reutilization of results and enables an efficient lab organization. It also supports the process of learning the inner context of different process control related subjects.
Apart from a few exceptions, only professors do the teaching at UAS, including not only lectures, but also exercises and labs. They are supported by additional instructors/assistants. In order to be able to work at a UAS, professors must have a doctorate degree and at least five years of career experience, including at least three years in industry. Teaching obligations of UAS professors amount to 18 hours/week in the teaching periods. Professors active in research have the option of reducing their teaching load. UAS do not offer PhD or doctorate programs on their own, since institutional laws pertaining to earning of doctorates are part of the core area of TU’s autonomy. But especially qualified UAS graduates can be admitted to doctoral studies, and an increasing number of UAS collaborates with TU’s in PhD programs as well.
1. INTRODUCTION In Germany, higher education in engineering sciences is offered both at Technical Universities (TU) and at Universities of Applied Sciences (UAS, in German: Fachhochschule). Most of the UAS in West Germany have been established around 1970 responding to new challenges in the workplace resulting from scientific and technical progress and pertinent new training requirements. The East German states began establishing UAS in 1991. Austria and Switzerland adopted this model in the 90s as well. The educational mission and profile of UAS can be summarized as follows (BMBF, 2003):
close connection between theory and practice (e.g., the vast majority of programs include a mandatory one semester internship in industry, almost all classes include comprehensive practical labs in addition to lectures and exercises, Bachelor/Master Thesis works are done in industry or in common industry/university projects) efficient organization of studies and examinations leading to shorter duration of study application-oriented research and development, technology and knowledge transfer to small and medium-sized enterprises. About two thirds of German engineers graduate from a UAS. UAS graduates meet industry’s needs for specialists with higher education qualifications and practically oriented training. As a result, many companies, in recruiting graduates, do not differentiate too much between UAS and TU qualifications. Initial salary levels for UAS graduates are largely the same as for TU ones. Since TU engineering programs are research-oriented and more challenging with regard to mathematical foundations, TU graduates will often be employed in companies R&D groups, while UAS graduates have a higher share in project planning and execution, and in technical sales departments.
978-3-902823-01-4/12/$20.00 © 2012 IFAC
The specific characteristics of UAS engineering education lead to a higher share of lab exercises in the general teaching and study workload. At the same time, lab exercises are not only oriented to reinforce learning of scientific basics, but also to get hands-on experience and professional skills for later employment in industry. The importance of lab exercises (both local and remote access, real and simulated plants) for control and instrumentation education is widely recognised (Bencomo, 2004, Edgar et al., 2006a). An overview of laboratory experiments and simulation examples used in undergraduate and graduate process control education is given in (Edgar et al., 2006a and 2006b). In (Rivera et al., 1996), the use of a Honeywell TDC3000 DCS in conjunction with different pilot plants in a senior-level course “Process Dynamics and Control” at Arizona State University (ASU) was described. At that time (e.g. mid of the 90s), the installation at ASU could only be enabled by a more than $1 million donation from industry. In this paper, a newly developed laboratory is described which can repeatedly be used in different classes of control engineering education at the undergraduate and graduate levels. It consists of a quadruple tank laboratory process, an 378
10.3182/20120619-3-RU-2024.00004
9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
industrial-scale DCS, and a number of Matlab/Simulink tools. Using the same pilot plant, control system and programming environment in different modules lowers the costs, allows the reutilization of results and enables an efficient lab organization. It also supports the process of learning the inner context of different process control related subjects.
The structure of the DCS connected to the quadruple tank system is shown in Fig. 2.
This paper is organized as follows: the pilot plant and DCS configurations are described in Section 2. In Section 3, selected lab exercises carried out with this equipment will be presented which reflect the specific needs of control education at a UAS. Section 4 presents how the scope of the laboratory was extended with the help of Matlab/Simulink tools connected via an OPC interface. 2. QUADRUPLE-TANK SYSTEM AND DCS The quadruple-tank system which is used as a pilot plant in the process control lab is shown in Fig. 1. Fig. 2. Process control laboratory DCS structure The distributed control system consists of a Honeywell C200 controller, an Experion PKS station and an Experion PKS server. The EPKS station is used as the “operator” interface. The EPKS server holds the real-time data base of the DCS and is also used as the computer for engineering the controller and operator station software. Both are Honeywell certified Dell PCs running Windows 2000 SP4 as operating system. For the connection between the C200 controller, the station and the server, a fault tolerant Ethernet connection is used. The server is also connected to a PC pool in another building via the university LAN. From this pool, up to 12 students are able to engineer the system in parallel and to test it in simulation mode. Of course, only one group at a time may have access to the laboratory process.
Fig. 1. Quadruple-tank system at West Coast UAS Heide It consists of a water reservoir, four cylindrically shaped tanks, four speed-controlled pumps and five ultrasonic level transmitters. This laboratory process has first been described in (Johansson, 2000) and is used in a number of universities word-wide (Vadigepalli et al., 2001, Rusli et al., 2004 and Felder et al., 2008). In most labs, two pumps are used, and the flow rates to tanks 1 and 2 are set by three-way valves. In our lab (as well as at the University of Stuttgart), four pumps are used instead, and the flow rate ratios are set by speed ratio controllers.
3. SELECTED LAB EXERCISES AND STUDENT PROJECTS The Department of Electrical and Information Engineering (EIE) offers a seven semester Bachelor program in electrical and information engineering including a mandatory one semester industrial internship (fifth semester). Within this Bachelor program, starting from the fourth semester, students can select between two profiles: Automation and Applied Information Engineering (AAI), and Microlelectronic Systems. In addition, the department offers a three-semester Master program “Industrial Automation” in collaboration with Wismar University of Technology.
In contrast to many other control labs, the plant is not connected to a PC or PLC/PC SCADA system, but to an industrial-scale DCS (Honeywell Experion PKS), of course in its smallest possible configuration. The reasons for the selection of this relatively expensive instrumentation are:
The AAI path of the Bachelor program next to classes in Signals and Systems, Process Dynamics and Control, Computer Networks and Bus Systems, Software Engineering, Sensors and Measurement, Power Electronics/Electrical machines and Drives, includes a course in Industrial Control Systems/PLCs and DCS. It is taught in the 4 th semester and includes 3h lectures, 1h exercises and 4h lab per week.
students can get hands-on experience with a “real” DCS as one of the most important forms of industrial-scale control system equipment, in particular for the process industries, the same equipment can be used in different laboratory courses for different tasks, i.e. the costs are shared by a number of labs (note that - in contrast to the ASU lab the cost of our DCS plus pilot plant installation is less than 50.000 EUR). 379
9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
The structure of the three semester collaborative Master program “Industrial Automation” is shown in Table 1. Additional elective courses offered include Building Automation, Modelling of Dynamic Systems, Real-Time Software Technology, and Enterprise Resource Planning/Manufacturing Execution Systems.
training programs for engineering professionals. Students can use the comprehensive DCS user manuals. In addition, a hand-out is provided where all steps of DCS engineering are described in detail. The specific tasks to be solved in the lab exercises are
to design a cascade control loop including feedforward action for a simulated industrial furnace using the CFC environment (“Control Builder” in Honeywell terminology) to develop a graphical interface to visualize and to operate the control functions using the “Graphics Builder” to perform a “virtual commissioning” of the control functions. A simplified P&I diagram of the process and the control configuration is presented in Fig. 3. The product temperature is controlled by the primary controller which sets the setpoint for the secondary fuel gas pressure controller. The feed flow rate is considered the main measurable disturbance variable. The feedforward path is designed as a corrective action for the pressure setpoint. Fast students can extend this problem and replace the pressure control by a fuel gas to air flow ratio control structure, and/or include an exhaust gas oxygen controller.
Again, all mandatory courses include lab exercises or student projects, respectively. In the following subsections, examples for the use of the DCS/Pilot plant in a selected course of the Bachelor and Master programs will be presented in detail. Table 1: Master program Industrial Automation (mandatory classes) Winter Semester
Advanced Process Control, Motion Control, Computer Networks and Security, Industrial Image Processing, General Management
Summer Semester
Sensor Systems, Robotics, Microcontroller Technology, Computational Engineering, Quality and Project Management
3rd Semester
Master Thesis (in industry)
The furnace dynamics is simulated on the DCS itself based on simple standard function blocks. The design of the control structure is based on standard library PID blocks as well.. In contrast to the industrial environment, the pressure controller output is connected with the furnace simulation and not sent to an actuator. Similar, the pressure, temperature and flow signals are taken from the simulation, and not from transmitters.
3.1 Bachelor program: Industrial Control Systems/DCS course In the AAI path of the Electrical and Information Engineering Bachelor program, the DCS (without the four-tank system) is used in the Industrial Control Systems/PLCs and DCS course (4th semester). It is organized as a sequence of 4h lab exercises in the computer lab every alternate week (i.e. 2h/week in average, with 14 weeks/semester), maximum twelve students in parallel. During the same semester, students visit a 1h/week DCS lecture. Due to the large number of students normally taking this course, the four-tank system is demonstrated but not used as part of the lab exercises. The lab workload amounts to 60 hours per semester including preparation. The objectives of the lab are
to help understanding the purpose, structure, properties and engineering tools of industrial control systems used in the process industry to gain experience in using the main engineering tools such as continuous and sequential function chart (CFC/SFC) design of typical control functions, and tools for operator screen design to give an overview of other tools used for DCS engineering such as alarm and event management, OPC interface, management of DCS security and others. The specific DCS used is of minor importance in this course, since the basic principles are the same for DCSs of different vendors, and knowledge acquired can easily be transferred to other systems. And of course, it cannot be the objective of this lab to replace Honeywell’s or other vendors’ DCS
Fig. 3: Simplified P&I diagram of a furnace control problem The functionality of the design can be tested by switching the Control Builder from “project view” to “monitoring view”. In this view, it is possible to pursuit all signals in real-time. For the design of the operator screen, the students use the socalled “Graphics Builder”. Their tasks are
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to draw the P&I diagram using the graphics library
9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
instrumentation in the process industries, where single-loop PID controllers are designed to control inherently multivariable processes.
to create a “dynamic shape” for the PID controller (a typical which can be used for all PID controllers in the project) displaying PV, SP, OP and controller mode to include a call of PID controller standard faceplates generated by the DCS itself to include trend displays to monitor the signals Fig. 4 presents an example of an operator screen designed by a student group.
Similar to an industrial setting, the following steps have to be executed before the controllers can be tuned:
design of two PID loops in the Control Builder (CFC) of the DCS using standard data acquisition and PID function blocks execution of step tests for the individual loops by manipulating the controller outputs in manual mode identification of simple process models (first or second order plus deadtime) by manual techniques and/or by using system identification packages (either Matlab System Identification Toolbox or identification part of model based tuning tools). In the next steps, students may compare different approaches to PI controller tuning, namely
single-loop tuning without consideration of process interactions, i.e. the two controllers are treated independently from each other. For controller tuning, either tuning rules or commercially available PID tuning software may be applied. simultaneous or multi-loop tuning of PI controllers taking into account the process interactions. averaging level control tuning. 2) Design of static or dynamic decoupling control structures. Another student project is focused on the design of a simplified decoupling control structure as shown in the “inverted decoupling” version in Fig. 5 (Gagnon et al., 1998).
Fig. 4: Furnace operator screen example The tasks to be solved are intentionally chosen to consolidate and to deepen students’ knowledge in process dynamics and control taught one semester earlier. In particular, practical problems such as simple methods of process identification, PID controller tuning, selection of the specific PID control algorithm, controller modes and bumpless transfer are stressed. 3.2 Master program: Advanced Process Control (APC) course In the Master program, the DCS is used together with the quadruple-tank system in the “Advanced Process Control” course. In contrast to the Bachelor program, lab exercises are performed as student projects over the full semester, the student groups (usually 2…4 students) are responsible for project organization themselves. The project results are presented by each group in a series of oral presentation sessions at the end of the semester. Different experiences and tasks/results can be shared since all students take part in those sessions. The projects are different in contents but have more or less the same size and complexity with a working load of approximately 90h including self-study, execution of lab work and preparation of the oral presentation.
Fig. 5: Inverted decoupling control of the quadruple-tank system This approach requires the development of a (2x2) transfer function model of the process by either experimental identification or based on a theoretical model. The decouplers
D12 ( s)
The student projects concentrate on different approaches for the solution of four-tank-system multivariable control problems. In particular, the following approaches are studied:
G12 ( s) G ( s) , D21 ( s) 21 G11 (s) G22 (s)
(1)
can be designed manually if the transfer functions are first approximated by FOPDT models, and can then be realized using standard software function blocks on the DCS, e.g. using “variable dead-time lead-lag” blocks.
1) Decentralized PI control for the levels in tanks 1 and 2. This structure represents the traditional way of 381
9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
3) Centralized multivariable control using an MPC controller. A third and the most challenging project is to design and commission a multivariable model predictive controller (MPC) for the quadruple-tank system, see Fig. 6. The students are encouraged to use a commercial MPC (Honeywell Profit Controller) package. The core of any MPC controller is a multivariable process model. In the project, students execute a series of step tests changing the manipulated variables (pump speeds), record the step responses of the tanks levels and use the internal identification package of the MPC controller for system identification. Since an empirical process model has been developed at least once in previous projects, earlier results can be used for validation. Then, the MPC controller is designed and simulated in off-line mode. The last step is to connect the MPC controller inputs and outputs to the DCS and to test the controller in online mode. For this purpose, the existing OPC server of the DCS and the OPC client of the MPC package can be used. The operator interface for the MPC controller is developed automatically during the design phase and doesn’t require additional effort.
Fig. 7. Integration of Experion PKS and Matlab/Simulink via OPC interface technology In other areas of industrial control applications (e.g. automotive/mechatronic systems), Matlab/Simulink is extensively used for rapid control prototyping and hardwarein-the-loop (HIL) simulation. Using the extended environment described above, students can be familiarized with these concepts during their studies. For rapid control prototyping, Matlab/Simulink is used for the development of new algorithms, e.g. for identification, monitoring and control, and the Experion PKS/pilot plant system is used for testing them. For HIL simulation, control configurations and “operator” screens can be developed on Experion PKS, and tested by running them against Simulink based process simulations. In the following subsections, two student projects will be described in more detail. 4.1 Rapid Control Prototyping: Predictive Functional Control of the four-tank system Predictive Functional Control (PFC) is a simple model predictive control (MPC) algorithm for SISO applications (Richalet and O’Donovan, 2009). PFC is easy to understand, to implement and to tune, and a candidate to replace PID controllers for certain applications. Since it is not part of the standard DCS control software, the aim of the project is to develop a PFC software function block for Experion PKS. Project steps include:
Fig. 6: MPC control of the quadruple-tank system 4. INTEGRATION OF EXPERION PKS AND MATLAB/SIMULINK USING AN OPC INTERFACE During the last decades, Matlab/Simulink and its numerous toolboxes became the standard environment in scientific computing in education and research. During the course of their studies, students of the EIE Department become more and more familiar with the use of Matlab/Simulink. Therefore, it was an obvious consequence to integrate Experion PKS and Matlab/Simulink tools using the OPC interface technology. In our case, the existing Experion PKS OPC server provides real-time access to plant data, and the Matlab OPC toolbox can be used as OPC client to connect different Matlab/Simulink based applications. Since Matlab/Simulink licenses are already used for other courses, this integration provides a cheap possibility to extend the scope of the laboratory. The setup is presented in Fig. 7.
to understand the PFC concept by programming the PFC algorithm for a first order process with dead time, and by testing it in closed-loop simulation in Matlab, to connect the Matlab PFC code with Experion PKS via OPC toolbox commands, to run the control algorithm against a process simulation created by DCS function blocks to control a level in the four-tanks system by the Matlab PFC code to develop and test a PFC custom algorithm block for the DCS with features such as mode selection, bumpless transfer etc. The scope of the project can easily be extended to the development of a graphical user interface on either the Matlab/Simulink or the DCS side.
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9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
4.2 HIL simulation: Multi-loop PID control of a simulated FCC unit
= lowest rating to 5 = highest rating). Comments were given regarding the improvement of DCS documentation. Students have found the experience both challenging and worth-wile for their prospective work as control engineers in industry, and a necessary endorsement to the lectures. Based on a lot of discussions with alumni and control engineers in industry, a positive feedback is given to the use of industrial-scale equipment and commercial software in UAS education. Since a lot of graduates of the department apply for jobs in engineering and project execution groups of the neighbouring process industry companies and in automation engineering offices, they valuate the control lab as a great help to easily integrate in their later teams and to avoid the “reality shock” when making the transition from academia to industry. In the future, the laboratory will be modified and possibly extended to cover requirements and additional content for continuing control education in the process industry.
During the last decades, a substantial number of Matlab/ Simulink based process plant simulators for education and research have been published. For another student project in the APC course of the Master program, the simplified FCCU model described in (Bequette, 2003) has been adopted. It is a fully coupled two-input, two-output system including nonlinearities, unusual process dynamics, large process noise, and one order of magnitude different settling times for the two control variables. Nevertheless, this FCCU model is much easier to handle than a full-blown FCCU dynamic simulator used for e.g. operator training systems. Steps of the student project include:
establishing and testing a connection between the Simulink FCCU model and Experion PKS via the Matlab OPC Toolbox/Simulink OPC blockset the design of the control structure with PID or Profit Loop function blocks using the CFC environment (Control Builder) of Experion PKS the identification of the multivariable (2x2) process model using the Matlab System Identification Toolbox tuning the PID controllers with different multi-loop tuning methods, and closed-loop simulations to analyse the response to reference signals and load disturbances, and to study the robustness with regard to plant-model mismatch. As an example, Fig. 8 presents the Simulink block diagram containing the FCCU model subsystem and OPC Toolbox Simulink blocks for the configuration of the interface, for reading Experion PKS PID controller output signals, and for writing simulated process values to the DCS.
REFERENCES Bencomo, D. (2004). Control learning – present and future. Annual Reviews in Control, 28, 115 – 136. Bequette, W.B. (2003). Process Control – Modeling, Design and Simulation, Prentice Hall, Upper Saddle River. BMBF (2003). Universities of Applied Sciences in Germany. Published by the Federal Ministry of Education and Research (BMBF) of Germany. Bonn Edgar, T.F., et al. (2006a). Renovating the undergraduate process control course. Computers and Chemical Engineering, 153, 254 – 261. Edgar, T.F., Ogunnaike, B.A., Muske, K.R. (2006b). A global view of graduate process control education. Computers and Chemical Engineering, 30, 1763 – 1774. Felder, J. et al. (2008). Praktikum Mess- und Regel-technik: Anleitung zum Versuch Quadruple Tank, Institut für Mess- und Regeltechnik, ETH Zürich. (in German) Gagnon, E., Pomerleau, A., Desbiens, A. (1998). Simplified, ideal or inverted decoupling. ISA Transactions, 37, 265 – 276. Johansson, K.H.. (2000). The Quadruple-Tank Process: A Multivariable Laboratory Process with an Adjustable Zero. IEEE Trans. on Control Systems Technology, 8, 256 – 265. Rivera, D.E. et al. (1996). Teaching Process Dynamics and Control using an industrial-scale real-time computing environment. Computer Applications in Engineering Education, 4, 191 – 205. Richalet, J., O’Donovan, D. (2009). Predictive Functional Control, Springer, London. Rusli, E., Ang, S., Braatz, R.D. (2004). A quadruple tank process control experiment. Chemical Engineering Education, 38, 174 – 187. Shin, J. et al. (2008). Analytical design of a PI controller for constrained optimal regulatory control of inventory loop. Control Engineering Practice, 16, 1391 – 1397. Vadigepalli, E.P. Gatzke, F.J. Doyle III (2001). Robust control of a multivariable experimental four-tank system. Industrial and Engineering Chemistry Research, 40, 1916 – 1927.
Fig. 8. Simulink block diagram for the OPC interface between FCCU simulation and Experion PKS 5. CONCLUSION The control laboratory experiments and projects documented in this paper have been developed over the last four years. Student response data for the laboratory courses was obtained from departmental surveys administered at the end of the semester. The results of this survey were an average score of 4.1 for the overall value of the lab courses (on a range from 1 383
Use of an industrial distributed control system in conjunction with Matlab/Simulink for process control education Rainer Dittmar
West Coast University of Applied Sciences, Heide, Germany (Tel: +49 481 8555-325; e-mail: [email protected]).
Abstract: The paper demonstrates how a four-tank laboratory process equipped with a modern distributed control system (DCS) is used in different control engineering laboratories for the undergraduate and graduate levels. This includes the utilization of commercial tools for DCS engineering as well as the design and commissioning of traditional and advanced control solutions. Matlab/Simulink tools are connected via OPC to extend the laboratory scope with rapid control prototyping and hardware-in-the-loop simulation. Using the same pilot plant, DCS and scientific computing environment in different courses lowers the costs, allows the reutilization of results and enables an efficient lab organization. It also supports the process of learning the inner context of different process control related subjects.
Apart from a few exceptions, only professors do the teaching at UAS, including not only lectures, but also exercises and labs. They are supported by additional instructors/assistants. In order to be able to work at a UAS, professors must have a doctorate degree and at least five years of career experience, including at least three years in industry. Teaching obligations of UAS professors amount to 18 hours/week in the teaching periods. Professors active in research have the option of reducing their teaching load. UAS do not offer PhD or doctorate programs on their own, since institutional laws pertaining to earning of doctorates are part of the core area of TU’s autonomy. But especially qualified UAS graduates can be admitted to doctoral studies, and an increasing number of UAS collaborates with TU’s in PhD programs as well.
1. INTRODUCTION In Germany, higher education in engineering sciences is offered both at Technical Universities (TU) and at Universities of Applied Sciences (UAS, in German: Fachhochschule). Most of the UAS in West Germany have been established around 1970 responding to new challenges in the workplace resulting from scientific and technical progress and pertinent new training requirements. The East German states began establishing UAS in 1991. Austria and Switzerland adopted this model in the 90s as well. The educational mission and profile of UAS can be summarized as follows (BMBF, 2003):
close connection between theory and practice (e.g., the vast majority of programs include a mandatory one semester internship in industry, almost all classes include comprehensive practical labs in addition to lectures and exercises, Bachelor/Master Thesis works are done in industry or in common industry/university projects) efficient organization of studies and examinations leading to shorter duration of study application-oriented research and development, technology and knowledge transfer to small and medium-sized enterprises. About two thirds of German engineers graduate from a UAS. UAS graduates meet industry’s needs for specialists with higher education qualifications and practically oriented training. As a result, many companies, in recruiting graduates, do not differentiate too much between UAS and TU qualifications. Initial salary levels for UAS graduates are largely the same as for TU ones. Since TU engineering programs are research-oriented and more challenging with regard to mathematical foundations, TU graduates will often be employed in companies R&D groups, while UAS graduates have a higher share in project planning and execution, and in technical sales departments.
978-3-902823-01-4/12/$20.00 © 2012 IFAC
The specific characteristics of UAS engineering education lead to a higher share of lab exercises in the general teaching and study workload. At the same time, lab exercises are not only oriented to reinforce learning of scientific basics, but also to get hands-on experience and professional skills for later employment in industry. The importance of lab exercises (both local and remote access, real and simulated plants) for control and instrumentation education is widely recognised (Bencomo, 2004, Edgar et al., 2006a). An overview of laboratory experiments and simulation examples used in undergraduate and graduate process control education is given in (Edgar et al., 2006a and 2006b). In (Rivera et al., 1996), the use of a Honeywell TDC3000 DCS in conjunction with different pilot plants in a senior-level course “Process Dynamics and Control” at Arizona State University (ASU) was described. At that time (e.g. mid of the 90s), the installation at ASU could only be enabled by a more than $1 million donation from industry. In this paper, a newly developed laboratory is described which can repeatedly be used in different classes of control engineering education at the undergraduate and graduate levels. It consists of a quadruple tank laboratory process, an 378
10.3182/20120619-3-RU-2024.00004
9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
industrial-scale DCS, and a number of Matlab/Simulink tools. Using the same pilot plant, control system and programming environment in different modules lowers the costs, allows the reutilization of results and enables an efficient lab organization. It also supports the process of learning the inner context of different process control related subjects.
The structure of the DCS connected to the quadruple tank system is shown in Fig. 2.
This paper is organized as follows: the pilot plant and DCS configurations are described in Section 2. In Section 3, selected lab exercises carried out with this equipment will be presented which reflect the specific needs of control education at a UAS. Section 4 presents how the scope of the laboratory was extended with the help of Matlab/Simulink tools connected via an OPC interface. 2. QUADRUPLE-TANK SYSTEM AND DCS The quadruple-tank system which is used as a pilot plant in the process control lab is shown in Fig. 1. Fig. 2. Process control laboratory DCS structure The distributed control system consists of a Honeywell C200 controller, an Experion PKS station and an Experion PKS server. The EPKS station is used as the “operator” interface. The EPKS server holds the real-time data base of the DCS and is also used as the computer for engineering the controller and operator station software. Both are Honeywell certified Dell PCs running Windows 2000 SP4 as operating system. For the connection between the C200 controller, the station and the server, a fault tolerant Ethernet connection is used. The server is also connected to a PC pool in another building via the university LAN. From this pool, up to 12 students are able to engineer the system in parallel and to test it in simulation mode. Of course, only one group at a time may have access to the laboratory process.
Fig. 1. Quadruple-tank system at West Coast UAS Heide It consists of a water reservoir, four cylindrically shaped tanks, four speed-controlled pumps and five ultrasonic level transmitters. This laboratory process has first been described in (Johansson, 2000) and is used in a number of universities word-wide (Vadigepalli et al., 2001, Rusli et al., 2004 and Felder et al., 2008). In most labs, two pumps are used, and the flow rates to tanks 1 and 2 are set by three-way valves. In our lab (as well as at the University of Stuttgart), four pumps are used instead, and the flow rate ratios are set by speed ratio controllers.
3. SELECTED LAB EXERCISES AND STUDENT PROJECTS The Department of Electrical and Information Engineering (EIE) offers a seven semester Bachelor program in electrical and information engineering including a mandatory one semester industrial internship (fifth semester). Within this Bachelor program, starting from the fourth semester, students can select between two profiles: Automation and Applied Information Engineering (AAI), and Microlelectronic Systems. In addition, the department offers a three-semester Master program “Industrial Automation” in collaboration with Wismar University of Technology.
In contrast to many other control labs, the plant is not connected to a PC or PLC/PC SCADA system, but to an industrial-scale DCS (Honeywell Experion PKS), of course in its smallest possible configuration. The reasons for the selection of this relatively expensive instrumentation are:
The AAI path of the Bachelor program next to classes in Signals and Systems, Process Dynamics and Control, Computer Networks and Bus Systems, Software Engineering, Sensors and Measurement, Power Electronics/Electrical machines and Drives, includes a course in Industrial Control Systems/PLCs and DCS. It is taught in the 4 th semester and includes 3h lectures, 1h exercises and 4h lab per week.
students can get hands-on experience with a “real” DCS as one of the most important forms of industrial-scale control system equipment, in particular for the process industries, the same equipment can be used in different laboratory courses for different tasks, i.e. the costs are shared by a number of labs (note that - in contrast to the ASU lab the cost of our DCS plus pilot plant installation is less than 50.000 EUR). 379
9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
The structure of the three semester collaborative Master program “Industrial Automation” is shown in Table 1. Additional elective courses offered include Building Automation, Modelling of Dynamic Systems, Real-Time Software Technology, and Enterprise Resource Planning/Manufacturing Execution Systems.
training programs for engineering professionals. Students can use the comprehensive DCS user manuals. In addition, a hand-out is provided where all steps of DCS engineering are described in detail. The specific tasks to be solved in the lab exercises are
to design a cascade control loop including feedforward action for a simulated industrial furnace using the CFC environment (“Control Builder” in Honeywell terminology) to develop a graphical interface to visualize and to operate the control functions using the “Graphics Builder” to perform a “virtual commissioning” of the control functions. A simplified P&I diagram of the process and the control configuration is presented in Fig. 3. The product temperature is controlled by the primary controller which sets the setpoint for the secondary fuel gas pressure controller. The feed flow rate is considered the main measurable disturbance variable. The feedforward path is designed as a corrective action for the pressure setpoint. Fast students can extend this problem and replace the pressure control by a fuel gas to air flow ratio control structure, and/or include an exhaust gas oxygen controller.
Again, all mandatory courses include lab exercises or student projects, respectively. In the following subsections, examples for the use of the DCS/Pilot plant in a selected course of the Bachelor and Master programs will be presented in detail. Table 1: Master program Industrial Automation (mandatory classes) Winter Semester
Advanced Process Control, Motion Control, Computer Networks and Security, Industrial Image Processing, General Management
Summer Semester
Sensor Systems, Robotics, Microcontroller Technology, Computational Engineering, Quality and Project Management
3rd Semester
Master Thesis (in industry)
The furnace dynamics is simulated on the DCS itself based on simple standard function blocks. The design of the control structure is based on standard library PID blocks as well.. In contrast to the industrial environment, the pressure controller output is connected with the furnace simulation and not sent to an actuator. Similar, the pressure, temperature and flow signals are taken from the simulation, and not from transmitters.
3.1 Bachelor program: Industrial Control Systems/DCS course In the AAI path of the Electrical and Information Engineering Bachelor program, the DCS (without the four-tank system) is used in the Industrial Control Systems/PLCs and DCS course (4th semester). It is organized as a sequence of 4h lab exercises in the computer lab every alternate week (i.e. 2h/week in average, with 14 weeks/semester), maximum twelve students in parallel. During the same semester, students visit a 1h/week DCS lecture. Due to the large number of students normally taking this course, the four-tank system is demonstrated but not used as part of the lab exercises. The lab workload amounts to 60 hours per semester including preparation. The objectives of the lab are
to help understanding the purpose, structure, properties and engineering tools of industrial control systems used in the process industry to gain experience in using the main engineering tools such as continuous and sequential function chart (CFC/SFC) design of typical control functions, and tools for operator screen design to give an overview of other tools used for DCS engineering such as alarm and event management, OPC interface, management of DCS security and others. The specific DCS used is of minor importance in this course, since the basic principles are the same for DCSs of different vendors, and knowledge acquired can easily be transferred to other systems. And of course, it cannot be the objective of this lab to replace Honeywell’s or other vendors’ DCS
Fig. 3: Simplified P&I diagram of a furnace control problem The functionality of the design can be tested by switching the Control Builder from “project view” to “monitoring view”. In this view, it is possible to pursuit all signals in real-time. For the design of the operator screen, the students use the socalled “Graphics Builder”. Their tasks are
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to draw the P&I diagram using the graphics library
9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
instrumentation in the process industries, where single-loop PID controllers are designed to control inherently multivariable processes.
to create a “dynamic shape” for the PID controller (a typical which can be used for all PID controllers in the project) displaying PV, SP, OP and controller mode to include a call of PID controller standard faceplates generated by the DCS itself to include trend displays to monitor the signals Fig. 4 presents an example of an operator screen designed by a student group.
Similar to an industrial setting, the following steps have to be executed before the controllers can be tuned:
design of two PID loops in the Control Builder (CFC) of the DCS using standard data acquisition and PID function blocks execution of step tests for the individual loops by manipulating the controller outputs in manual mode identification of simple process models (first or second order plus deadtime) by manual techniques and/or by using system identification packages (either Matlab System Identification Toolbox or identification part of model based tuning tools). In the next steps, students may compare different approaches to PI controller tuning, namely
single-loop tuning without consideration of process interactions, i.e. the two controllers are treated independently from each other. For controller tuning, either tuning rules or commercially available PID tuning software may be applied. simultaneous or multi-loop tuning of PI controllers taking into account the process interactions. averaging level control tuning. 2) Design of static or dynamic decoupling control structures. Another student project is focused on the design of a simplified decoupling control structure as shown in the “inverted decoupling” version in Fig. 5 (Gagnon et al., 1998).
Fig. 4: Furnace operator screen example The tasks to be solved are intentionally chosen to consolidate and to deepen students’ knowledge in process dynamics and control taught one semester earlier. In particular, practical problems such as simple methods of process identification, PID controller tuning, selection of the specific PID control algorithm, controller modes and bumpless transfer are stressed. 3.2 Master program: Advanced Process Control (APC) course In the Master program, the DCS is used together with the quadruple-tank system in the “Advanced Process Control” course. In contrast to the Bachelor program, lab exercises are performed as student projects over the full semester, the student groups (usually 2…4 students) are responsible for project organization themselves. The project results are presented by each group in a series of oral presentation sessions at the end of the semester. Different experiences and tasks/results can be shared since all students take part in those sessions. The projects are different in contents but have more or less the same size and complexity with a working load of approximately 90h including self-study, execution of lab work and preparation of the oral presentation.
Fig. 5: Inverted decoupling control of the quadruple-tank system This approach requires the development of a (2x2) transfer function model of the process by either experimental identification or based on a theoretical model. The decouplers
D12 ( s)
The student projects concentrate on different approaches for the solution of four-tank-system multivariable control problems. In particular, the following approaches are studied:
G12 ( s) G ( s) , D21 ( s) 21 G11 (s) G22 (s)
(1)
can be designed manually if the transfer functions are first approximated by FOPDT models, and can then be realized using standard software function blocks on the DCS, e.g. using “variable dead-time lead-lag” blocks.
1) Decentralized PI control for the levels in tanks 1 and 2. This structure represents the traditional way of 381
9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
3) Centralized multivariable control using an MPC controller. A third and the most challenging project is to design and commission a multivariable model predictive controller (MPC) for the quadruple-tank system, see Fig. 6. The students are encouraged to use a commercial MPC (Honeywell Profit Controller) package. The core of any MPC controller is a multivariable process model. In the project, students execute a series of step tests changing the manipulated variables (pump speeds), record the step responses of the tanks levels and use the internal identification package of the MPC controller for system identification. Since an empirical process model has been developed at least once in previous projects, earlier results can be used for validation. Then, the MPC controller is designed and simulated in off-line mode. The last step is to connect the MPC controller inputs and outputs to the DCS and to test the controller in online mode. For this purpose, the existing OPC server of the DCS and the OPC client of the MPC package can be used. The operator interface for the MPC controller is developed automatically during the design phase and doesn’t require additional effort.
Fig. 7. Integration of Experion PKS and Matlab/Simulink via OPC interface technology In other areas of industrial control applications (e.g. automotive/mechatronic systems), Matlab/Simulink is extensively used for rapid control prototyping and hardwarein-the-loop (HIL) simulation. Using the extended environment described above, students can be familiarized with these concepts during their studies. For rapid control prototyping, Matlab/Simulink is used for the development of new algorithms, e.g. for identification, monitoring and control, and the Experion PKS/pilot plant system is used for testing them. For HIL simulation, control configurations and “operator” screens can be developed on Experion PKS, and tested by running them against Simulink based process simulations. In the following subsections, two student projects will be described in more detail. 4.1 Rapid Control Prototyping: Predictive Functional Control of the four-tank system Predictive Functional Control (PFC) is a simple model predictive control (MPC) algorithm for SISO applications (Richalet and O’Donovan, 2009). PFC is easy to understand, to implement and to tune, and a candidate to replace PID controllers for certain applications. Since it is not part of the standard DCS control software, the aim of the project is to develop a PFC software function block for Experion PKS. Project steps include:
Fig. 6: MPC control of the quadruple-tank system 4. INTEGRATION OF EXPERION PKS AND MATLAB/SIMULINK USING AN OPC INTERFACE During the last decades, Matlab/Simulink and its numerous toolboxes became the standard environment in scientific computing in education and research. During the course of their studies, students of the EIE Department become more and more familiar with the use of Matlab/Simulink. Therefore, it was an obvious consequence to integrate Experion PKS and Matlab/Simulink tools using the OPC interface technology. In our case, the existing Experion PKS OPC server provides real-time access to plant data, and the Matlab OPC toolbox can be used as OPC client to connect different Matlab/Simulink based applications. Since Matlab/Simulink licenses are already used for other courses, this integration provides a cheap possibility to extend the scope of the laboratory. The setup is presented in Fig. 7.
to understand the PFC concept by programming the PFC algorithm for a first order process with dead time, and by testing it in closed-loop simulation in Matlab, to connect the Matlab PFC code with Experion PKS via OPC toolbox commands, to run the control algorithm against a process simulation created by DCS function blocks to control a level in the four-tanks system by the Matlab PFC code to develop and test a PFC custom algorithm block for the DCS with features such as mode selection, bumpless transfer etc. The scope of the project can easily be extended to the development of a graphical user interface on either the Matlab/Simulink or the DCS side.
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4.2 HIL simulation: Multi-loop PID control of a simulated FCC unit
= lowest rating to 5 = highest rating). Comments were given regarding the improvement of DCS documentation. Students have found the experience both challenging and worth-wile for their prospective work as control engineers in industry, and a necessary endorsement to the lectures. Based on a lot of discussions with alumni and control engineers in industry, a positive feedback is given to the use of industrial-scale equipment and commercial software in UAS education. Since a lot of graduates of the department apply for jobs in engineering and project execution groups of the neighbouring process industry companies and in automation engineering offices, they valuate the control lab as a great help to easily integrate in their later teams and to avoid the “reality shock” when making the transition from academia to industry. In the future, the laboratory will be modified and possibly extended to cover requirements and additional content for continuing control education in the process industry.
During the last decades, a substantial number of Matlab/ Simulink based process plant simulators for education and research have been published. For another student project in the APC course of the Master program, the simplified FCCU model described in (Bequette, 2003) has been adopted. It is a fully coupled two-input, two-output system including nonlinearities, unusual process dynamics, large process noise, and one order of magnitude different settling times for the two control variables. Nevertheless, this FCCU model is much easier to handle than a full-blown FCCU dynamic simulator used for e.g. operator training systems. Steps of the student project include:
establishing and testing a connection between the Simulink FCCU model and Experion PKS via the Matlab OPC Toolbox/Simulink OPC blockset the design of the control structure with PID or Profit Loop function blocks using the CFC environment (Control Builder) of Experion PKS the identification of the multivariable (2x2) process model using the Matlab System Identification Toolbox tuning the PID controllers with different multi-loop tuning methods, and closed-loop simulations to analyse the response to reference signals and load disturbances, and to study the robustness with regard to plant-model mismatch. As an example, Fig. 8 presents the Simulink block diagram containing the FCCU model subsystem and OPC Toolbox Simulink blocks for the configuration of the interface, for reading Experion PKS PID controller output signals, and for writing simulated process values to the DCS.
REFERENCES Bencomo, D. (2004). Control learning – present and future. Annual Reviews in Control, 28, 115 – 136. Bequette, W.B. (2003). Process Control – Modeling, Design and Simulation, Prentice Hall, Upper Saddle River. BMBF (2003). Universities of Applied Sciences in Germany. Published by the Federal Ministry of Education and Research (BMBF) of Germany. Bonn Edgar, T.F., et al. (2006a). Renovating the undergraduate process control course. Computers and Chemical Engineering, 153, 254 – 261. Edgar, T.F., Ogunnaike, B.A., Muske, K.R. (2006b). A global view of graduate process control education. Computers and Chemical Engineering, 30, 1763 – 1774. Felder, J. et al. (2008). Praktikum Mess- und Regel-technik: Anleitung zum Versuch Quadruple Tank, Institut für Mess- und Regeltechnik, ETH Zürich. (in German) Gagnon, E., Pomerleau, A., Desbiens, A. (1998). Simplified, ideal or inverted decoupling. ISA Transactions, 37, 265 – 276. Johansson, K.H.. (2000). The Quadruple-Tank Process: A Multivariable Laboratory Process with an Adjustable Zero. IEEE Trans. on Control Systems Technology, 8, 256 – 265. Rivera, D.E. et al. (1996). Teaching Process Dynamics and Control using an industrial-scale real-time computing environment. Computer Applications in Engineering Education, 4, 191 – 205. Richalet, J., O’Donovan, D. (2009). Predictive Functional Control, Springer, London. Rusli, E., Ang, S., Braatz, R.D. (2004). A quadruple tank process control experiment. Chemical Engineering Education, 38, 174 – 187. Shin, J. et al. (2008). Analytical design of a PI controller for constrained optimal regulatory control of inventory loop. Control Engineering Practice, 16, 1391 – 1397. Vadigepalli, E.P. Gatzke, F.J. Doyle III (2001). Robust control of a multivariable experimental four-tank system. Industrial and Engineering Chemistry Research, 40, 1916 – 1927.
Fig. 8. Simulink block diagram for the OPC interface between FCCU simulation and Experion PKS 5. CONCLUSION The control laboratory experiments and projects documented in this paper have been developed over the last four years. Student response data for the laboratory courses was obtained from departmental surveys administered at the end of the semester. The results of this survey were an average score of 4.1 for the overall value of the lab courses (on a range from 1 383