Appendix A MoReRT Controllers Design Demo Software


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1 Appendix A MoReRT Controllers Design Demo Software The use of the proposed ModelReference Robust Tuning (MoReRT) design methodology, described in Chap. 4, to tune a twodegreeoffreedom (2DoF) proportional integral derivative (PID) controller requires of an optimization program, as outlined in Sect In order to facilitate the implementation of the MoReRT approach, a MATLAB based software package has been developed. The provided routines just require the user to input the process information data and desired controller structure. The software will perform the required optimizations and show the closedloop responses for the obtained controller. In the following, a simple MoReRT software package implemented in MAT LAB is described. The user interface and software usage are explained by means of developing some design examples. This software can be obtained directly from the authors. A.1 Introduction Considered the general closedloop control system depicted in Fig. A.1 where the controlled process is given by y(s) = P u (s)u(s) + P d (s)d(s), (A.1) and the controller output signal by the expression u(s) = C r (s)r(s) + C y (s)y(s). (A.2) From (A.1) and (A.2) the closedloop control system output is then y(s) = C r(s)p u (s) 1 + C y (s)p u (s) r(s) + P d (s) d(s), (A.3) 1 + C y (s)p u (s) Springer International Publishing Switzerland 2016 V.M. Alfaro and R. Vilanova, ModelReference Robust Tuning of PID Controllers, Advances in Industrial Control, DOI /
2 170 Appendix A: MoReRT Controllers Design Demo Software Fig. A.1 General 2DoF closedloop control system block diagram that can be rewrite in compact form as y(s) = M yr (s)r(s) + M yd (s)d(s). (A.4) If P d (s) = P u (s) the disturbance d(s) actsasaninput disturbance d i (s), or load disturbance. In case that the disturbance input dynamics P d (s) = 1 the disturbance d(s) is an output disturbance d o (s). In general, if P d (s) = P u (s) the path from the disturbance to the controlled variable d y is different to the path from the controller output signal to the controlled variable u y. The controlled process model P u (s) captures the controlled variable dynamics to a change in the controller output signal and the model P d (s) the corresponding dynamics to a change in the process disturbance signal. These two dynamics can be very different. Therefore, (A.3) allows to analyze different alternatives or control problems. A.2 Controlled Process Models and Control Algorithm We consider here the general case of overdamped first and secondorder plus deadtime controlled process models, (F)SOPDT, and a controller with a 2DoF Standard PID control algorithm, PI(D) 2. Controlled Process Models The controlled process dynamics are given by following models: P u (s) = P d (s) = K pu e L pus (T pu s + 1)(a pu T pu s + 1), (A.5) K pd e L pds (T pd s + 1)(a pd T pd s + 1), (A.6) where their parameters are θ pu = { K pu = 0, T pu > 0, 0 a pu 1, L pu 0 } and θ pd = { K pd = 0, T pd 0, 0 a pd 1, L pd 0 }. Here the usual default parameter values for the derivative part have been taken.
3 Appendix A: MoReRT Controllers Design Demo Software 171 Gains of the controlled process models P u (s) and P d (s) can be any number different from zero. If K pu > 0 the controller Action must be set to Reverse (+). In case that K pu < 0 a Direct ( ) Action is required for the controller. Control Algorithm The 2DoF Standard PID control algorithm given by the equation or { u(t) = K p βr(t) y(t) + 1 t [r(ξ) y(ξ)]dξ + T i { u(s) = K p βr(s) y(s) + 1 [r(s) y(s)]+ T T i s 0 } d[γ r(t) y(t)], (A.7) dt } ds [γ r(s) y(s)] αt d s + 1 (A.8) is selected for the controller. Then, the controller parameters to tune are θ c = { K p > 0, T i > 0, T d 0, β 0, α= 0.1, γ= 0 }. The setpoint and feedback controllers are given by the transfer functions: ( C r (s) = K p β + 1 ) T i s C y (s) = K p ( T i s +, (A.9) ). (A.10) T d s 0.1T d s + 1 Normalized Controller Process Models and Controllers As described in Chap. 5 it is convenient to normalize the controlled process models and the controller. Then, using P u (s) parameters K pu and T pu and the transformation ŝ = T pu s, the normalized versions of A.5, A.6, A.9, and A.10 are: ˆP u (ŝ) = e τ Lpuŝ (ŝ + 1)(a pu ŝ + 1), (A.11) κ pd e ˆP Lpdŝ d (ŝ) = (τ pd ŝ + 1)(a pd τ pd ŝ + 1), (A.12) ( Ĉ r (ŝ) = κ p β + 1 ), τ i ŝ (A.13) ( Ĉ y (ŝ) = κ p ) τ i ŝ + τ d ŝ, 0.1τ d ŝ + 1 (A.14)
4 172 Appendix A: MoReRT Controllers Design Demo Software where τ Lpu κ pd. L pu =, T pu. K pd =,τ pd K pu. = T pd T pu,τ Lpd κ p. = K pu K p,τ i. = T i T pu,τ d. = L pd T pu,. = T d T pu, (A.15) are the new normalized (dimensionless) parameters. A.3 ClosedLoop Transfer Functions Targets and Cost Functionals Following the general procedure described in Chap. 4 and in Sect for the particular case when P d (s) = P u (s), the target closedloop servocontrol and regulatory control transfer functions for (A.4) are selected as: Myr t (ŝ) =. (τ c ŝ + 1) e τ Lpuŝ (τc 2ŝ2 + 2ζτ c ŝ + 1)(a pu τ c ŝ + 1), (A.16) Myd t (ŝ) =. (ŝ + 1)(a pu ŝ + 1) (κ pd /κ pu )(τ i /κ p ) ŝ e τ pdŝ (τ pd ŝ + 1)(a pd τ pd ŝ + 1) (τc 2ŝ2 + 2ζτ c ŝ + 1)(a pu τ c ŝ + 1), (A.17) where θ d = {ζ,τ c } are the design parameters. According to the pursued studies and analysis, presented in the corresponding chapters, there are recommended fixed values for ζ in order to guarantee a good compromise between the performance and control signal usage. On the other hand, τ c should be adjusted in order to provide the fastest response for the desired robustness. A.4 MoreRT Controllers Design Software Implementation As shown in Fig to implement the proposed MoReRT design procedure a minimum of five routines are required: a main program for data entry, a design function to call the optimization function and for iteration control, a cost function, and a simulation and plotting function. The MoReRT cost function optimization is done using MATLAB fminsearch function which uses the NelderMead simplex method. The control system simulation can be made directly in MATLAB or using a Simulink block diagram.
5 Appendix A: MoReRT Controllers Design Demo Software 173 Data Entry The user must provide the following information: The controlled process models P u (s) and P d (s) parameters: θ pu and θ pd, respectively. The controller initial normalized parameters: θc 0 (i.e. κ0 p = 1,τo i = 1,τd 0 = 0.25,β = 1). The design parameters θ d ={ζ,τ c }. For nearly nonoscillating responses ζ = 1 must be used. For under damped responses it is recommended to use ζ = 0.80 for the PI and ζ = 0.7 for the PID. The closedloop relative speed τ c can be a single value (i.e. τ c = 1), a set of discrete values (i.e. τ c = 0.8, 1.0, 1.05), or a sequence of values (i.e. τ c = 0.8 : 0.1 : 1.5). The simulation time control (total simulation time and the discretization (sampling) time). The simulation span must cover the time required for the target servocontrol and the regulatory control response to reach to a new steadystate operation. The discretization time selection impacts the total CPU time required for the cost function optimization process but also the numerical solution accuracy and stability. Cost Function The cost function evaluation include following steps: Regulatory control M yd (s) step response, yyd m (t). Regulatory control target Myd t (s) step response, yt yd (t). Regulatory control cost functional, J d = [yyd m (t) yt yd (t)]2 dt. Servocontrol M yr (s) step response, yyr m (t). Servocontrol target Myr t (s) step response, yt yr (t). Servocontrol cost functional, J r = [yyr m (t) yt yr (t)]2 dt. Total cost functional evaluation, J T = J d + J r. Controller Parameters For each τ c given, the optimization routine prints the obtained controller normalized parameters ˆθ c. With these, the corresponding control system robustness M S is evaluated. For the last design parameter τ c analyzed, the controller MoReRT tuning parameters (not the normalized ones) K P = κ p /K pu, T i = τ i T pu, T d = τ d T pu, β, α = 0.1, and γ = 0 are returned. Finally, the control system responses to a Δr setpoint change followed by a Δd disturbance change are shown with its robustness plot. The MoReRT designed controller parameters {K p, T i, T d,β} are available at the MATLAB Workspace as the variables Kpo, Tio, Tdo, and bo, respectively. Demo Software Files MoReRTcPID2pSOPuPd.p: main program, data entry and program execution control.
6 174 Appendix A: MoReRT Controllers Design Demo Software Fig. A.2 Program main menu user interface mrtfpcpid2psopupd.p: design parameter τ c iteration control, optimization function call. mrtfccpid2psopupd.p: cost function evaluation. mrtfscpid2psopupd.p: control systems simulation, step responses and robustness plot. mrtfgnyquist.p: Nyquist with M S circles plot. MATLAB functions: bode, disp, fminsearch, lsim, nyquist, plot, step. Program Main Menu The MoReRT demo software user interface ([MAIN MENU]) is shown in Fig. A.2. Using the MAIN MENU push buttons the user can input the problem data (design parameters, controlled process (F)SOPDT models parameters, PI(D) controller initial parameters, and simulation control), run the controller tuning routine, and/or exit the control system design process. Input Data Error Detection The MoReRT demo software implements a simple verification of the input data provided by the user to prevent an eventual program malfunction or hanging. Some of the error messages are shown in Fig. A.3. A.5 MoReRT Controllers Design Demo Software Usage For use of the MoReRT controllers design demo software consider the following example
7 Appendix A: MoReRT Controllers Design Demo Software 175 Fig. A.3 Input data error messages Controlled process models: 2e 2.0s P u (s) = (5.0s + 1)(2.5s + 1), P d (s) = P u (s). Controller: 2DoF PI. Design criteria: MS t 1.80, ζ = 0.80, τ c = 1.0 (only one initial test). Controller initial normalized parameters: κ 0 p = 1.0, τ i 0 = 1.0, τd 0 = 0, β = 1.0. Simulation control: t u = δt = 0.02 (for simulations during optimization), t us = δt s = 0.2, Δr = 10 %, Δd = 5 % (for final control system simulation). Design for Input Disturbance For the first example, the disturbance transfer function is selected as P d (s) = P u (s). Then, we are considering an input disturbance d i (s). The Execution of the MoReRT Design Demo Software maim program is started writing the main file name at MATLAB Command Window:»MoReRTcPID2pSOPuPd [Enter] After that, the program Main Menu shown in Fig. A.2 is displayed. Data Input User must provide the control problem data. The problem data input windows are selected from the MAIN MENU: [DESIGN Parameters], opens the window shown in Fig. A.4. [CONTROLLED PROCESSES Parameters], opens the window shown in Fig. A.5.
8 176 Appendix A: MoReRT Controllers Design Demo Software Fig. A.4 Design parameters input window Fig. A.5 Controlled process P u (s) parameters input window [CONTROLLER Parameters], opens the window shown in Fig. A.6. [SIMULATION Control], opens the window shown in Fig. A.7. Program Output The program output is: == MoReRT Controllers Design (cpid2/psopdt) == ============================================== Controlled process models parameters Pu(s): Kpu = 2 Tpu = 5 apu = 0.5 Lpu = 2
9 Appendix A: MoReRT Controllers Design Demo Software 177 Fig. A.6 PI Controller initial normalized parameters input window Fig. A.7 Control system simulation control input window Pd(s) = Pu(s) Design parameters controller: PI2 z = 0.8 tco = 1 MoReRT controller normalized parameters tc = 1
10 178 Appendix A: MoReRT Controllers Design Demo Software kp = ti = td = 0 ba = Ms = == MoReRT PID2 controller parameters == Action = reverse (+) Kp = Ti = Td = 0 alpha = 0.1 beta = gamma = 0 Ms = 1.573» The robustness of the control system M S = 1.57 is higher that the design requirement. Then, we can increase the control system speed and use tcv = 0.95:0.05:0.85, to obtain three controllers. Therefore looking for a better performance on the basis of the desired robustness. Selecting from the MAIN MENU the [DESIGN Parameters] input window the new closedloop relative speed design parameters are typed as shown in Fig. A.8 Design Progress Bar During the design process its progress is indicated by a length changing red bar as showninfig.a.9. The design process can be interrupted with the [Cancel] push button. It will be stopped after that the optimization with the next τ c design parameter is finished. Fig. A.8 Change of the closedloop relative speeds
11 Appendix A: MoReRT Controllers Design Demo Software 179 Fig. A.9 Controller design progress bar New Results The program output is now: == MoReRT Controllers Design (cpid2/psopdt) == ==============================================... MoReRT controller normalized parameters tc = 0.95 kp = ti = td = 0 ba = Ms = tc = 0.9 kp = ti = td = 0 ba = Ms = tc = 0.85 kp = 1.1 ti = td = 0 ba = Ms = == MoReRT PID2 controller parameters == Action = reverse (+) Kp = 0.55 Ti = Td = 0 alpha = 0.1
12 180 Appendix A: MoReRT Controllers Design Demo Software y(t), r(t), u(t), d(t) [%] MoReRT Control System Performance y(t) r(t) u(t) d(t) L (jω) Robustness M S =1.2 M =2.0 S 1 L(jω) M = S L(jω) time, t [controlled process model time units] L (jω) Fig. A.10 Demo example (d i ) System responses and robustness (τ c = 0.85) beta = gamma = 0 Ms = » For τ c = 0.85 the control system robustness is M S = Then, we select a PI 2 controller with parameters K p = 0.55, T i = 6.49, and β = The resulting control systems responses and robustness are shown in Fig. A.10. Design for Output Disturbance To design the MoReRT controller considering an output disturbance d o the transfer function P d (s) = 1. Then, Kpd = 1, Tpd = 0, apd = 0, and Lpd = 0 are automatically set. Selecting at the [DESIGN Parameters] input window the control problem 2 (Pd = 1) and using z = 0.8 and tcv = 0.85 as shown in Fig. A.11,wehave == MoReRT Controllers Design (cpid2/psopdt) == ==============================================... MoReRT controller normalized parameters tc = 0.85 kp = ti = td = 0 ba = Ms = == MoReRT PID2 controller parameters == Action = reverse (+)
13 Appendix A: MoReRT Controllers Design Demo Software 181 Fig. A.11 Output disturbance selection Kp = Ti = Td = 0 alpha = 0.1 beta = gamma = 0 Ms = » Using the same closedloop system relative speed design parameter, the resulting control system for an output disturbance is more oscillating, with higher peak error, and less robust than the one designed by considering an input disturbance. The new control systems responses and robustness are shown in Fig. A.12. To compare, at the same design robustness level, the control system obtained considering an output disturbance with the one obtained earlier considering an input disturbance the control system relative speed must be decreased. Adjusting tc we finally obtain: == MoReRT Controllers Design (cpid2/psopdt) == ============================================== Controlled process models parameters Pu(s): Kpu = 2 Tpu = 5 apu = 0.5 Lpu = 2 Pd(s) = 1
14 182 Appendix A: MoReRT Controllers Design Demo Software y(t), r(t), u(t), d(t) [%] MoReRT Control System Performance y(t) r(t) u(t) d(t) L (jω) Robustness M S =1.2 M S =2.0 1 L(jω) M = S L(jω) time, t [controlled process model time units] L (jω) Fig. A.12 Demo example (d o ) System responses and robustness (τ c = 0.85) Design parameters controller: PI2 z = 0.8 tco = MoReRT controller normalized parameters tc = kp = ti = td = 0 ba = Ms = == MoReRT PID2 controller parameters == Action = reverse (+) Kp = Ti = Td = 0 alpha = 0.1 beta = gamma = 0 Ms = » The responses of the new controller are shown in Fig. A.13. The parameters of the two PI 2 controllers are listed in Table A.1. These two controller parameters sets are different but produce control systems with the same robustness level.
15 Appendix A: MoReRT Controllers Design Demo Software 183 y(t), r(t), u(t), d(t) [%] MoReRT Control System Performance y(t) r(t) u(t) d(t) L (jω) Robustness M S =1.2 M S =2.0 1 L(jω) M = S L(jω) time, t [controlled process model time units] L (jω) Fig. A.13 Demo example (d o ) New system responses and robustness (τ c = 0.895) Table A.1 MoReRT PI 2 parameters Disturbance τ c K p T i β M S d i d o Design for a Slow Disturbance Consider now that the main disturbance dynamics has been identified and that it is slower than early assumed and with a negative and lower gain. It is given by the model 0.5e 4.0s P d (s) = (7.5s + 1)(5.625s + 1). The robustness requirement is increased to MS t = The only data that needs to be changed is shown if Figs. A.14 ([DESIGN Parameters]) and A.15 ([CONTROLLED PROCESSES Parameters]). The program output data is:» MoReRT_cPID2pSOPuPd == MoReRT Controllers Design (cpid2/psopdt) == ============================================== Controlled process models parameters Pu(s): Kpu = 2 Tpu = 5 apu = 0.5 Lpu = 2
16 184 Appendix A: MoReRT Controllers Design Demo Software Fig. A.14 New desing parameters Fig. A.15 Controlled process data (P d (s) = Pu(s))
17 Appendix A: MoReRT Controllers Design Demo Software 185 Pd(s): Kpd = 0.5 Tpd = 7.5 apd = 0.75 Lpd = 4 Design parameters controller: PI2 z = 0.8 tco = 1 MoReRT controller normalized parameters tc = 1 kp = ti = td = 0 ba = Ms = tc = 0.9 kp = ti = td = 0 ba = Ms = tc = 0.8 kp = ti = td = 0 ba = Ms = tc = 0.75 kp = ti = td = 0 ba = Ms = == MoReRT PID2 controller parameters == Action = reverse (+) Kp = Ti = Td = 0
18 186 Appendix A: MoReRT Controllers Design Demo Software y(t), r(t), u(t), d(t) [%] MoReRT Control System Performance y(t) r(t) u(t) d(t) L (jω) Robustness M S =1.2 M =2.0 S 1 L(jω) M = S L(jω) time, t [controlled process model time units] L(jω) Fig. A.16 Demo example P d (s) = P u (s) (slow disturbance) System responses and robustness alpha = 0.1 beta = gamma = 0 Ms = » The PI 2 control system response obtained with τ c = 0.75 has the required robustness M S = Its response is shown in Fig. A.16. It is noted that in this case the controller proportional setpoint weight needs a value β>1(beta =1.145). MoReRT PID 2 Controller For comparison with the PI 2 controller PID 2 controllers are obtained using z = 0.70 and tcv = 0.65:0.05:0.75. The changes in the input data are shown in Figs. A.17 ([DESIGN Parameters]) and A.18 ([CONTROLLER Parameters]). The PID design gives: == MoReRT controllers design (cpid2/psopdt) == ==============================================... Design parameters controller: PID2 z = 0.7 tco = MoReRT controller normalized parameters tc = 0.65
19 Appendix A: MoReRT Controllers Design Demo Software 187 Fig. A.17 New design parameters for PID 2 Fig. A.18 PID controller normalized initial parameters kp = ti = td = ba = Ms = tc = 0.7 kp = ti = td = ba = Ms =
20 188 Appendix A: MoReRT Controllers Design Demo Software tc = 0.75 kp = ti = td = ba = Ms = == MoReRT PID2 controller parameters == Action = reverse (+) Kp = Ti = Td = alpha = 0.1 beta = gamma = 0 Ms = » Parameters of the PI 2 and PID 2 controllers are listed in Table A.2. For the same robustness, the PID 2 control system provides a faster disturbance recovery and with Table A.2 MoReRT PI 2 and PID 2 parameters Controller K p T i T d β M S PI PID y(t), r(t), u(t), d(t) [%] MoReRT Control System Performance y(t) r(t) u(t) d(t) L (jω) Robustness M S =1.2 M =2.0 S 1 L(jω) M = S L(jω) time, t [controlled process model time units] L (jω) Fig. A.19 Demo example PID 2 (P d (s) = P u (s)) System responses and robustness
21 Appendix A: MoReRT Controllers Design Demo Software 189 lower maximum error than the PI 2. Its setpoint response is also faster but with a higher overshoot, as seen in Fig. A.19. Select [EXIT] from the MAIN MENU to close the program and return to MAT LAB.
22 Index C Closedloop reference models, 33, 147 Commercial controllers, 7, 55, 129, 145, 165 Control algorithm, 7, 15, 16, 42 proportional integral, 7, 8, 42, 107 proportional integral derivative, 7 9, 11, 12, 15, 16, 18, 23, 39, 55, 129, 132, 169 Control effort, 1, 8, 18, 21, 23, 27, 32, 34, 52, 53, 60, 61, 64, 70, 72, 74 77, 82, 87, 89, 91, 96, 100, 101, 105, 106, 109 Controlled process integrating, 4, 79, 91, 93, 105, 141 inverse response, 4, 37, 65, 68, 70, 75, 114, 141, 142, 145, 148 overdamped, 3, 4, 37, 41, 53, 55, 60, 84, 105, 107, 109, 145 unstable, 4, 93 96, 105, 145 Controlled process models firstorder plus deadtime, 36, 37, 39 41, 47, 55, 60, 96 99, 108, 136 secondorder plus dead time, 36, 37, 40 Controlled variable, 1, 3, 7, 22, 23, 30, 33, 43, 84, 105, 106, 109, 111, 112, 114, 119, 120, 155, 165, 170 Controller parallel, 39 PI, 42, 50, 60, 62, 72, 74, 77, 79, 81, 83, 96, 119, 158 PID, 2, 3, 13, 14, 16, 32, 35, 36, 41, 55, 56, 60, 64, 74, 75, 77, 86, 96, 105, 111, 119, 126, 129, 130, 132, 137, 140, 149 standard, 39, 119, 132, 140 Control system, 2, 21 27, 29, 30, 32, 33, 42, 52, 59, 75, 80, 93, 94, 96, , 108, 115, 117, 118, 125, 126, 147, 148, 156, 178, 188 Cost functionals, 31, 108, 172 D Deadtime compensating, 119, 120, 125, 126 Disturbance, 1, 7, 23, 30, 51, 70, 88, 89, 105, 109, 111, 112, , 120, 122, 126, 130, 146, 147, 159, 163, 170, 173, 175, 181 E Evaluation metrics control effort, 21, 23 fragility, 21, 24 performance, 2, 3, 41, 70 robustness, 21, 23, 173 F Fragility, 3, 21, M Maximum sensitivity, 3, 24, 25, 32, 56, 105, 108, 122, 125 Modelreference, 4, 31, 32, 34, 59, 62 N Normalized controlled process model, 40, 129 Normalized controller, 36, 38, 39, 66, 82, 171 P Performance indices, 49, 62, 75, 100, 158 Springer International Publishing Switzerland 2016 V.M. Alfaro and R. Vilanova, ModelReference Robust Tuning of PID Controllers, Advances in Industrial Control, DOI /
23 192 Index R Regulatory control, 3, 7, 26, 30, 31, 33, 34, 42, 43, 49 51, 58, 60, 70, 71, 75, 76, 86, 87, 90, 112, 148, 172 Robustness, 3, 18, 23 25, 27, 32, 43, 44, 49, 50, 60, 61, 66, 70, 72, 75, 76, 81, 85, 87, 89, 96, 99, 102, 105, 108, 109, 111, 117, 121, 122, 126, 132, 141, 146, 148, 158, 163, 165 S Servocontrol, 3, 7, 16, 26, 30, 32, 34, 42, 43, 51, 52, 54, 59, 62, 75, 86, 89, 93, 105, 112, 121, 122, 125, 140, 165, 172, 173 Setpoint, 1, 3, 7, 10, 18, 21 23, 30, 33, 34, 48, 59, 60, 70, 86, 89, 101, 102, 105, 108, 111, , 135, 140, 159, 165, 186 Stirredtank heater, 149, 165 T Tuning rule, 2, 3, 7, 12, 23, 25, 27, 35, 40, 81, 97, 101, 129, 140, 141, 148, 165 Twodegreeoffreedom, 2, 3, 9, 30, 43, 116, 129, 130, , 137, 139, 140, 165
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