UNICOS CPC PID Auto-Tuning Manual

Transcription

1 CERN CH1211 Geneva 23 Switzerland EDMS NO. REV. VALIDITY DRAFT REFERENCE PID AUTO-TUNING Date : Manual UNICOS CPC PID Auto-Tuning Manual DOCUMENT PREPARED BY: DOCUMENT CHECKED BY: DOCUMENT APPROVED BY: Ruben Marti Martinez Benjamin Bradu Enrique Blanco Vinuela [BE/ICS/PCS] [BE/ICS/PCS] [BE/ICS/PCS] William Booth [BE/ICS/PCS]

2 Page 2 of 21 HISTORY OF CHANGES REV. NO. DATE PAGES DESCRIPTIONS OF THE CHANGES First version (Ruben Marti Martinez) Added new Section. Further features added to Autotuning

3 TABLE OF CONTENTS Page 3 of 21 UNICOS CPC PID... 1 Auto-Tuning Manual... 1 TABLE OF FIGURES INTRODUCTION Tool implementation Basics concepts DIRECT/INVERSE ACTION SAMPLING TIME Safety limitations AUTOTUNING METHODS Relay Method RELAY METHOD PROCEDURE SIMC Method SIMC METHOD PROCEDURE IFT Method IFT METHOD PROCEDURE Further features Reference... 21

4 Page 4 of 21 TABLE OF FIGURES Figure 1. PID autotuning tool architecture Figure 2. Direct/Reverse PID Configuration Figure 3. Autotuning Faceplate Figure 4. Process Dynamic information. Time constant Figure 5. Autotuning Limits Figure 6. Relay method interface Figure 7. Relay method configuration Figure 8. Autotuning pop-up panel Figure 9. SIMC method configuration Figure 10. Identification panel. SIMC method Figure 11. Identification pop-up panel. SIMC method Figure 12. IFT method interface Figure 13. Extended IFT s parameters Figure 14. IFT pop-up panel Figure 15. Autotuning notification Figure 16. Relay warning message Figure 17. Tracking or Local Mode Warning Message Figure 18. Current Status Info Message Figure 19. Access right configuration

5 Page 5 of INTRODUCTION The goal of this document is to describe how to use PID Autotuning tool for a UNICOS CPC6 application. The PID (Proportional, Integral and Derivative) controller is the most frequently used feedback control algorithm in the process control industry, despite its age and thanks to its simplicity and efficiency. The major challenge in using PID control is to find the optimal set of parameters to tune the controller. This may be a complex task for a non-expert as it mostly depends on the dynamics of the process being controlled. In this document a tool to automatically provide the users with an appropriate set of PID parameters is presented. The tool offers auto-tuning methods, both in open and closed loop, and others could be added in the future. The tool is fully integrated into the UNICOS CPC framework and can be used to tune multiple controllers at the same time, directly from the WinCC OA supervision [1]. 1.1 Tool implementation The PID structure taken into consideration to design the autotuning tool is the ISA version used in the UNICOS CPC framework. The control law can be seen in the formula (1). OOOO = KK cc TT ii ss + sstt dd 1 + sstt EE(ss) (1) dd TT dddd The tool is fully integrated in the UNICOS CPC framework at the supervision layer, in WinCC-OA SCADA. The main characteristic is that the PID algorithm remains untouched in the PLC (control layer) and only its parameters are set once the tuning process is finished and validated. The architecture and data flow is depicted in Figure 1. Figure 1. PID autotuning tool architecture.

6 Page 6 of 21 The human interaction is made through dedicated interfaces embedded in the UNICOS CPC controller faceplate. The user can select the method, configure its options, initiate the autotuning, evaluate the behaviour and ultimately set the new parameters to the controller. 1.2 Basics concepts The PID auto-tuning concept refers to the capability to automatically compute the parameters of a PID connected to the real plant. Many different approaches are available. This work focused on developing an auto-tuning tool launched by the operators or the control engineers on their own initiative, performing the chosen tuning algorithm and presenting the results to the user who ultimately decides to apply them to the controller. The behaviour of an auto-tuning algorithm is based on what an expert controls engineer would do when tuning that loop by hand. First a phase of observation, then in function of the control objectives selection of the method and finally applying the PID parameters. The first phase includes process data taking and this is done by stimulating the process either by moving the manipulated variable or the controller setpoint. Therefore, the first concept to mention is open and close loop methods. Open loop autotuning algorithms deactivate the PID controller in order to move the manipulated variable and get a process identification and based on this information, the PID s parameters will be obtained. On the other hand, in Close loop methods, the PID is controlling the process and the setpoint is changed to measure the change in performance Direct/Inverse action Another important concept is the direct/inverse action. Whether a controller needs to be direct or reverse acting is determined by the configuration of the process. In the case of a tank level control, for example, it depends on the placement of the control valve. If the valve controls the flow out of the tank, we would want a positive error (level too high) to increase the control output, opening the valve and letting more fluid out of the tank; thus, a direct acting controller would be used. If the valve controls the flow into the tank, however, a reverse acting controller would be used, which would respond to a high level by closing the valve and reducing the flow into the vessel. This information is included in the Specs file but also can be modified on the PID faceplate as is shown in Figure 2. To modify this parameters, a button called Para is included on the PID faceplate, see Figure 3, and once it is pushed a configuration panel is shown, Figure 2.

7 Page 7 of 21 Figure 2. Direct/Reverse PID Configuration. Figure 3. Autotuning Faceplate Sampling Time The last important concept is related to sampling time, which corresponds to how often the PID controllers gets new process measurements and computes and transmits the new PID s output. Generally speaking, a sampling time which is too big will have a negative impact on performance. On the other hand, a sampling time which is small will not necessarily provide better performance, though it may result in the auto-tuning method failing to compute the correct PID parameters. Big and small are relative terms defined by the process time constant, T p. The Figure 4 shows the main dynamic information of a systems, to obtain them the PID controller has to be in manual mode and out positioning. Then the user will change the controller output and the dynamic response will be recorded. The time constant (T p) is defined as time to reach the 63% of the final value of the steady state.

8 Page 8 of 21 Figure 4. Process Dynamic information. Time constant. Therefore, one of the best practice for sampling time is to define it ten times smaller than the process time constant or less. TT SSSSSSSSSSSSSSSS 0.1TT pp (2) For instance, if the controller is controlling the temperature in a room, the time spent to reach a steady state may be more than one hour, then normally the PID s sampling time will be 6 minutes. On the other hand, if the controller is controlling the flow manipulating a valve, this kind of process will spend around several seconds to reach the steady state, for instances 20 seconds, then the PID s sampling time should be configured to 2 seconds. This information is normally indicated in the Specs file but also can be modified using the same Para button. Note that, for old versions of CPC projects (generated with RP or before) this information is not in WinCC OA so a default value is defined. Please, check this value before starting an autotuning process. 1.3 Safety limitations The PID autotuning tool provides its own limits to stop the autotuning process if either the output value (OV) or/and the measured value (MV) cross the upper or/and lower bounds. To configure these limits the user has to push the Tuning Limits button on the PID autotuning faceplate, Figure 3.

9 Page 9 of 21 Then, a configuration panel will open, where the user can define the corresponding limits, as can be seen in Figure 5. Note that, these limits should be tighter than PID s limits in order to avoid unsafe situations. Figure 5. Autotuning Limits. 2. AUTOTUNING METHODS The selection of a method depends on the control needs which, in turn, depends on the analysis of the control problem. There is no general rule although the methods available in the toolbox can cope with the majority of the processes as they are rather universal. Three methods have been included: Relay, SIMC and IFT. Relay method will be used when the user does not have any information about the behaviour of the process and there is no previous PID tuned parameters (the Relay method provides aggressive PID parameters). SIMC method will be used when the user does not have any information about the dynamic behaviour of the process. It can also be used to get a specific response thanks to its simplicity, having only one tuning parameter. The SIMC method needs a model of the process to be used, and this can be generated using the tool itself, which executes a simple step-response. IFT method will be used when the control loop is critical and the user prefers that the PID controller be controlling during the tuning. In this case, the controller setpoint is manipulated, within given limits. On the other hand, the IFT

10 Page 10 of 21 method can be used when the user wants to optimize the performance of the PID controller. 2.1 Relay Method The relay auto-tuning method was proposed to automate the Ziegler-Nichols ultimate cycling tuning method [2], where the critical gain (Kcr) and period (Pcr) are determined with a proportional-only regulator. The controller gain is gradually increased until an oscillation is obtained Relay Method Procedure To tune a PID controller using relay method the following steps have to be followed: 1. Open the PID faceplate and select the Autotuning tab, then the user has to choose the RELAY method and finally select the architecture of the PID controller (PI or PID) to tune Figure 6. Relay method interface. 2. Then, the user has to configure the relay method s parameters. The first ones correspond to the maximum (dmax) and minimum (dmin) value of the manipulated variable. Note that, the maximum output has to be greater than the current output and the other hand, the minimum value has to be less than the current output (Figure 7). Otherwise, the method will not work properly (a warning message will be shown to the user and the method will not start). The last one (Cycles) corresponds to how many iterations the method will execute for the tuning.

11 Page 11 of Figure 7. Relay method configuration. 3. To launch the autotuning process, click Start. Once it is launched a new popup panel, where the autotuning method information is summarized, see Figure 8. Using this information panel, the user can monitoring the current state of the controller. In addition, the autotuning method parameters are shown. Finally, the current state of the method: Running or Finish are presented. The pop-up panel allows users to stop the autotuning process at any moment, by pushing the Stop button. Besides, the user can open the PID s faceplate using Controller button. Note that, the autotuning method can be stopped using either the PID faceplate Stop button or the pop-up panel mentioned above. On the other hand

12 Page 12 of 21 Figure 8. Autotuning pop-up panel. 4. Once the autotuning process has finished, the user has to apply the new PID s parameters, using Apply button on the right side of the PID s faceplate panel, see Figure 7. The advantage of the Relay method is its simplicity. The user only has to introduce the amplitude of the hysteresis and the number of cycles (Figure 6). The method is appropriate for processes with unknown dynamics and the method can be used to get initial albeit slightly aggressive PID parameters. If further PID tuning is required, the user can use either of the following methods, for example. 2.2 SIMC Method Another well-known group of auto-tuning methods is based on plant models [3]. Basically, two different steps compose the methodology, first a process identification phase and second the application of the tuning rules. Obviously this approach requires a process model SIMC Method Procedure To tune a PI (just PI structure) controller using SIMC method the following steps have to be followed:

13 Page 13 of Open the PID faceplate and select the Autotuning tab, then the user has to choose the SIMC method Figure 9. SIMC method configuration. 2. The next step is to identify a process model, pushing Identify button a new panel is open and it helps user to get a proper model. Note that, SIMC method needs a process model to subsequently tune the values of the new PID parameters, so if the user already has a model, continue the procedure in point 3. To identify a model the following steps have to be followed: a) The user has to define the desired step-change in the output signal, see Figure 10. The panel shows the user the limits of the output signal (indicating the maximum and minimum step values). b) Finally, pushing Start button the identification method is launched, opening a pop-up panel, which summarises the main information about the process, see Figure 11.

14 Page 14 of Figure 10. Identification panel. SIMC method. Figure 11. Identification pop-up panel. SIMC method. Using this information panel, the user can monitoring the current state of the identification process. On the other hand, the main identification s parameter is shown. Finally, the current state of the method: Running or Finish are shown.

15 Page 15 of 21 In addition, the pop-up panel allows the users to stop the identification process at any moment, by pushing the Stop button. Besides, the user can open the PID s faceplate using the Controller button. Note that, the autotuning method can be stopped by using either the PID s faceplate Stop button, or the Stop button on the pop-up panel mentioned above. The identification method includes an automatic way to detect the steady state, so once the identification is finished the controller will automatically go back to Auto/Regulation mode. However, if the user does not want to wait or steady state is not reached, the identification procedure can be stopped ( Stop button) and based on the gathered information a model will be proposed. c) Finally, the model identified is shown and the user can go back to SIMC method by pushing the SIMC button. 3. Once the model has been obtained using the above method (or the user can set values manually by pushing Edit ) the SIMC auto-tuning rules can be used. By moving the slider, the user selects the desired performance of the controller (FAST-ROBUST behaviour), see dynamic step-response plot. Once the user lets go of the slider, the proposed new PID parameters are shown. 4. Finally, to accept the new parameters the user has to push Apply button. Note that, SIMC has been implemented only for PI controllers. The SIMC method was chosen due to its advantages regarding others model-based techniques [4], mainly its simplicity in terms of parameterisation: SIMC has just a single tuning parameter, the desired performance. Its selection is a trade-off between performance ( tight control) and robustness ( smooth control). Therefore, the operators or control engineers can easily choose what kind of response they desire in the feedback control loop. The method is applicable to stable and low order systems with no complex dynamics. 2.3 IFT Method The previous two methods have some notable drawbacks: for the relay method, significant disturbances are introduced to the process and, for the SIMC method, the need of a model of the plant. Also the algorithms need to disable the PID controller while the auto-tuning process is running. These limitations justify the inclusion of an additional method in the tool that ideally does not require knowledge of any explicit model of the plant and whose execution would not significantly disturb the nominal loop dynamics. The IFT method [5] is a technique inspired by the iterative parametric optimisation approach. It is entirely working in closed-loop and making random but contained perturbations in the loop to find the optimal parameters. It computes a cost function to minimise the current value of the measured value and a desired first order response.

16 Page 16 of IFT Method Procedure To tune a PID controller using IFT method the following steps have to be followed: 1. Open PID faceplate and select the Autotuning tab, then the user has to choose the IFT method and finally select the architecture of the PID controller (PI or PID) to tune Figure 12. IFT method interface. 2. Then, the user has to configure the IFT method s parameters. The first one ( Alpha ) is used to setup the desired performance, by moving the slider, the user selects the desired performance of the controller (FAST-ROBUST behaviour). Once the user lets go of the slider the proposed IFT parameters are shown. The second parameter corresponds to the desired delay (Note that, the delay has to be greater than 1, otherwise the IFT will no work properly). Note, pressing the Default button resets the parameters back to default values of the IFT method. Moreover, pushing Parameters button an extended configuration panel is opened, see Figure 13. The first parameter, Cycles corresponds to how many iterations is going to spend the method for tuning the PID controller. The parameter Setpoint Step corresponds to the change in the active setpoint in order to measure the performance of the current PID s parameters.

17 Page 17 of 21 Finally, Control Effort corresponds to an expert parameter to penalize the control effort in the manipulated variable (default value 0). The parameters have to be applied using Apply button. Figure 13. Extended IFT s parameters. 3. To launch the autotuning process, click Start. Once it is launched a pop-up panel, where the main autotuning method information is summarized, see Figure 14. Using this information panel, the user can monitoring the current state of the controller. On the other hand, the main tuning method parameters are shown. Finally, the current state of the method: Running or Finish are presented. In addition, the pop-up panel allows users to stop the autotuning process at any moment, by pushing the Stop button. Besides, the user can open the PID s faceplate using the Controller button. Note that, the autotuning method can be stopped using either the Stop button on the PID faceplate or the Stop button on the pop-up panel mentioned above.

18 Page 18 of 21 Figure 14. IFT pop-up panel. 4. Once the autotuning process has finished, the user can restore the previous PID s parameters, using the Restore button on the right side of the PID s faceplate panel, see Figure 13. The IFT algorithm is particularly suitable for industrial process control use as it fulfils the requirements of being a model-free and closed-loop procedure which does not demand a very severe modification of the nominal conditions. The algorithm has the capability of efficiently rejecting disturbances, crucial for the regulatory control widely deployed in process industries. 3. Further features Besides autotuning methods explained before, CPC PID Autotuning includes some features to ease the use of itself and notice the users how is going on. First one corresponds to how to know that an autotuning method is running on a specific controller object. For that reason, a new animation on the widget has been implemented. When a controller is using autotuning to find the PID s parameters, the controller s widget will blink to notify the users that autotuning is running. Moreover, a led green icon has been included in the Autotuning tab, as is shown in Figure 15. On the other hand, if the autotuning is running on a controller and its faceplate is open, the faceplate will be initialized in Autotuning tab.

19 Page 19 of 21 Figure 15. Autotuning notification. The second features added corresponds to check before starting the methods whether the current setup is correct. For Relay method, the faceplate checks if the maximum output is greater than the current output and the minimum value is less than the current output as well. If not, a warning message will be shown as in Figure 16. Figure 16. Relay warning message.

20 Page 20 of 21 Next, for all the methods the controller s status is checked before launching the methods. If the controller is in Tracking or Local mode a warning message will be shown as in Figure 17. Figure 17. Tracking or Local Mode Warning Message. In addition, for all methods the controller s status and mode are checked and summarized in a pop-up message (Figure 18), where the current state to prevent launch the autotuning. Figure 18. Current Status Info Message.

21 Page 21 of 21 The last features added is how to setup the access right to Autotuning. For instance in Figure 19 is shown how to setup Autotuning just for admin access, just written AutoTune action on the corresponding action. Figure 19. Access right configuration. 4. Reference [1] R. Martí, B. Bradu, E. Blanco Vinuela, L. Frutos, R. Mazaeda, C. Prada. PID_TUNE: A PID autotuning software tool on UNICOS CPC. Proceedings of the 15th International Conference on Accelerator and Large Experimental Physics Control Systems, Melbourne, Australia, [2] J.G. Ziegler and N.B. Nichols. Optimum setting for automatic controllers. Transactions of the ASME, 64, [3] K. Aström and T. Hägglund. Automatic tuning of simple regulators on phase and amplitude margins. Automatica, 20, [4] A. O Dwyer. Handbook of PI and PID Controller Tuning Rules. Imperial College Press, [5] S. Dormido and F. Morilla. Auto-tuning and Anti-windup methods in PID controllers. UNED Press, [6] H. Hjalmarsson, M. Gevers and O. Lequin. Iterative feedback tuning: Theory and applications. IEEE Control Systems Magazine 18(4), 1998.