PID control of dead-time processes: robustness, dead-time compensation and constraints handling
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1 PID control of dead-time processes: robustness, dead-time compensation and constraints handling Prof. Julio Elias Normey-Rico Automation and Systems Department Federal University of Santa Catarina IFAC PID 18 Ghent May IFAC Conference on Advances in Proportional-Integral-Derivative Control
2 Dead-time processes Dead-time processes are common in industry and other areas Main dead-time (or delay) causes are: Transportation dead time (mass, energy) Apparent dead time (cascade of low order processes) Communication or processing dead time PID control of dead-time processes: robustness, dead-time compensation and constraints handling 2/48
3 Control of dead-time processes Dead time makes closed-loop control difficult Simplest solution: PID - trade-off robustness and performance Basic dead-time compensator - Smith Predictor (SP) Improved solutions: Modified SP (ex. FSP) Advanced solution: Model Predictive Control - MPC Most used in industry PID DTC MPC * Industry 4.0 complex controllers at low level * A Survey on Industry Impact and Challenges Thereof. IEEE CONTROL SYSTEMS MAGAZINE 17 PID control of dead-time processes: robustness, dead-time compensation and constraints handling 3/48
4 When to use advanced control? DEAD-TIME PROCESSES PID simple process models DTC compensates dead-time and can use high order models MPC is optimal and consider constraints Objectives: Analysis of PID, DTC and MPC for dead-time processes PID control of dead-time processes: robustness, dead-time compensation and constraints handling 4/48
5 Agenda 1. Motivating examples, PID and DTC control. 2. of dead-time processes 3. using DTC ideas 1. Unified tuning using FSP (stable and unstable plants) 2. Trade-off performance-robustness 3. Comparing PID and DTC 4. MPC, FSP and PID controllers 1. Unconstrained case 2. Constrained case Using anti-windup 5. PID control of dead-time processes: robustness, dead-time compensation and constraints handling 5/48
6 Motivating examples
7 Simple model with large delay and large modelling error Simple model big delay DTC Robust PID and DTC tuning (slow response) PID Process Even for a dominant delay process PID offers a good response Control PID control of dead-time processes: robustness, dead-time compensation and constraints handling 7/48
8 Fast response small delay Simple model with small modelling error Well known delay (network) disturbance Fast (without oscillations) Process Even for a small delay DTC offers better response Control Robust DTC for the assumed modelling error PID control of dead-time processes: robustness, dead-time compensation and constraints handling 8/48
9 To study the advantages of advanced controllers for dead-time processes related to: Process dead-time Process modeling error (robustness) Other aspects: Model complexity Constraints handling PID control of dead-time processes: robustness, dead-time compensation and constraints handling 9/48
10 of deadtime processes
11 Smith predictor of a pure delay process R(s) _ C(s) Q(s) P(s) Y(s) Y p (s) G n (s) e -Ls _ 1 delay 2 delays PID control of dead-time processes: robustness, dead-time compensation and constraints handling 11/48
12 Smith predictor of a FOPDT process Open loop Pure delay G(s) L disturbance 2 L SP: Only stable plants and slow responses PID control of dead-time processes: robustness, dead-time compensation and constraints handling 12/48
13 Ideal Control Achievable Performance Normal index No controller can act before 2 L L td PID control of dead-time processes: robustness, dead-time compensation and constraints handling 13/48
14 Ideal Control Achievable Performance Normal index SETPOINT No controller can act before DISTURBANCE To compare controllers performance 2 L L td PID control of dead-time processes: robustness, dead-time compensation and constraints handling 13/48
15 Ideal Control Achievable Performance Normal index SETPOINT No controller can act before DISTURBANCE To compare controllers performance L td 2 L Ideal Jmin = 0 PID control of dead-time processes: robustness, dead-time compensation and constraints handling 13/48
16 How to achieve ideal response? Is it ideally possible to achieve Jmin = 0? R(s) F(s) _ C(s) Q(s) P(s) Y(s) Filtered Smith Predictor Y p (s) G n (s) e -Ls F r (s) _ PID control of dead-time processes: robustness, dead-time compensation and constraints handling 14/48
17 How to achieve ideal response? Is it ideally possible to achieve Jmin = 0? R(s) F(s) _ C(s) Q(s) P(s) Y(s) Filtered Smith Predictor Y p (s) The same Hyr as SP G n (s) e -Ls F r (s) _ PID control of dead-time processes: robustness, dead-time compensation and constraints handling 14/48
18 How to achieve ideal response? Is it ideally possible to achieve Jmin = 0? R(s) F(s) _ C(s) Y p (s) Q(s) The same Hyr as SP P(s) G n (s) e -Ls F r (s) _ Y(s) Filtered Smith Predictor The filter Fr(s) allows: Eliminates the open-loop dynamics from the input disturbance response FSP for unstable plants FSP for ramp and other disturbances Robustness-Performance trade-off PID control of dead-time processes: robustness, dead-time compensation and constraints handling 14/48
19 How to achieve ideal response? Is it ideally possible to achieve Jmin = 0? R(s) F(s) _ C(s) Y p (s) Q(s) The same Hyr as SP P(s) G n (s) e -Ls F r (s) _ Y(s) Filtered Smith Predictor The filter Fr(s) allows: Eliminates the open-loop dynamics from the input disturbance response FSP for unstable plants FSP for ramp and other disturbances Robustness-Performance trade-off Jmin=0! PID control of dead-time processes: robustness, dead-time compensation and constraints handling 14/48
20 Example: Integrative plant Ideal SP Ideal FSP PID control of dead-time processes: robustness, dead-time compensation and constraints handling 15/48
21 PID design using FSP Many FSP successful applications in practice:* Termo-solar systems, Compression systems, Neonatal Care Unit. FSP autotuning for simple process** Idea: To derive a for dead-time processes using the FSP approach PID is a low frequency approximation of the FSP. *Torrico, Cavalcante, Braga, Normey-Rico, Albuquerque, I&EC Res *Flesch, Normey-Rico, Control Eng. Practice, 2017 **Normey-Rico, Sartori, Veronesi,Visioli. Control Eng. Practice, 2014 * Roca, Guzman, Normey-Rico, Berenguel, Yebra, Solar Energy, 2011 PID control of dead-time processes: robustness, dead-time compensation and constraints handling 16/48
22 using FSP
23 Tuning procedure Process models: FOPDT, IPDT, UFOPDT PI primary controller (only P for the IPDT) FO predictor filter (tuning for step disturbances) Tuning for a delay-free-closed-loop system with pole (double pole) in s=-1/t0 T0 is the only tuning parameter for a trade-off robustness-performance T0 ROBUSTNESS PERFORMANCE PID control of dead-time processes: robustness, dead-time compensation and constraints handling 18/48
24 Tuning procedure Equivalent 2DOF controller r(t) Feq(s) _ e(t) Ceq(s) u(t) q(t) P(s) y(t) 2DOF PID PID control of dead-time processes: robustness, dead-time compensation and constraints handling 19/48
25 Tuning procedure Tuning advantages of the predictor-pid Unified approach for FOPDT, IPDT and UFOPDT (L<2T) It has only one tuning parameter T0* Has similar performance than well known methods* It is a low frequency approximation of the ideal solution for first order dead-time models Interesting method to use in comparisons with dead-time compensators and predictive controllers Next: To compare PID and FSP * Normey-Rico and Guzma n. Ind. & Eng. Chem. Res., 2013 * Astrom and Hagglund, Research Triangle Park, 2006 PID control of dead-time processes: robustness, dead-time compensation and constraints handling 20/48
26 FSP-PID comparative analysis Performance Index PID control of dead-time processes: robustness, dead-time compensation and constraints handling 21/48
27 Robustness FSP-PID comparative analysis magnitude modelling error r(t) _ Ceq(s) u(t) q(t) P(s) y(t) frequency Conservatism can be avoided separating dead-time uncertainties* *Larsson and Hagglund (2009), ECC 2008 PID control of dead-time processes: robustness, dead-time compensation and constraints handling 22/48
28 FSP-PID comparative analysis 10 2 Stable L/T=0.2 Lag dominant T =T T =T/3 0 0 T=5L JPID magnitude 10 0 modelling error frequency JFSP PID control of dead-time processes: robustness, dead-time compensation and constraints handling 23/48
29 FSP-PID comparative analysis 10 2 Stable L/T=0.2 Lag dominant T =T T =T/3 0 0 T=5L JPID magnitude 10 0 modelling error frequency JFSP 10 2 Stable L/T=5 Delay Tdominant =2T T =T 0 0 L=5T magnitude 10 0 modelling error frequency PID control of dead-time processes: robustness, dead-time compensation and constraints handling 23/48
30 FSP-PID comparative analysis 10 2 Stable L/T=0.2 Lag dominant T =T T =T/3 0 0 T=5L JPID magnitude 10 0 modelling error frequency JFSP 10 2 Stable L/T=5 Delay Tdominant =2T T =T 0 0 L=5T magnitude 10 0 modelling error frequency PID control of dead-time processes: robustness, dead-time compensation and constraints handling 23/48
31 FSP-PID comparative analysis 10 2 Stable L/T=0.2 Lag dominant T =T T =T/3 0 0 T=5L JPID magnitude 10 0 modelling error frequency JFSP 10 2 Stable L/T=5 Delay Tdominant =2T T =T 0 0 L=5T magnitude 10 0 modelling error frequency PID control of dead-time processes: robustness, dead-time compensation and constraints handling 23/48
32 FSP-PID comparative analysis Integrative plant 10 2 Unstable plant 10 2 magnitude modelling error frequency magnitude 10 0 modelling error frequency * Normey-Rico and Camacho, 2007, Springer PID control of dead-time processes: robustness, dead-time compensation and constraints handling 24/48
33 FSP-PID comparative analysis Tuning: Trade-off Robustness-Performance Minimise J for robust stability for a given modelling error Particular tuning using: Minimise J for robust stability for a given General tuning using -1 * Grimholt and Skogestad 2012, IFAC PID PID control of dead-time processes: robustness, dead-time compensation and constraints handling 25/48
34 FSP-PID comparative analysis Tuning: Trade-off Robustness-Performance Minimise J for robust stability for a given modelling error Particular tuning using: Minimise J for robust stability for a given General tuning using Control effort (total variation) and noise attenuation are directly related to robustness indexes as Rm (or MS)* -1 * Grimholt and Skogestad 2012, IFAC PID PID control of dead-time processes: robustness, dead-time compensation and constraints handling 25/48
35 FSP-PID comparative analysis Case 1: poor model information (large modelling error) - Simple model is used for tuning - High robustness is mandatory - Step disturbances PID will be the best solution, even for dead-time dominant systems Case 2: good model is available (small modelling error) - Fast responses are required - Low robustness is enough - Complex models or disturbances FSP will be better (even for lag-dominant systems) because of the PID nominal limitations PID control of dead-time processes: robustness, dead-time compensation and constraints handling 26/48
36 FSP-PID comparative analysis Concerning dead-time: dead-time value is less important than dead-time modelling error. Implementation issues: FSP is implemented as a 2DOF discrete controller FSP is a complex algorithm (delay order (in samples) model order) PID is simple to implement General problems in industry: large modelling error, noise, simple models and solutions Use a well tuned PID for dead-time processes PID control of dead-time processes: robustness, dead-time compensation and constraints handling 27/48
37 Example 1: High-order system Robust tuning for Ms=1.2 M s =1.2 Nominal Case PID(solid) FSP (dashed) Prediction Model for FSP disturbance 1 0 control action output using SWORD * tool FSP and PID have the same performance **Garpinger, O. and T. Hägglund (2015), Journal of Process Control. ** SWORD Matlab software tool. PID control of dead-time processes: robustness, dead-time compensation and constraints handling 28/48
38 Example 2: PID, SP and FSP SP and FSP with the same primary PID controller for min IAE for Ms=2 (using sword tool) Max. delay error 20% 1 disturbance M s =2, 40% better Control action Performance Analysis Open-loop oscillatory disturbance response 0-1 FSP 14% better 20 s SP FSP PID Process output FSP 40% better Robustness : FSP stable up to -35% or 35% delay error, SP unstable for 20% delay error PID control of dead-time processes: robustness, dead-time compensation and constraints handling 29/48
39 Example 2: PID, SP and FSP delay error 20% FSP disturbance PID Control action delay error -20% Process output FSP PID Control action Process output SP unstable for this case PID and FSP similar responses PID control of dead-time processes: robustness, dead-time compensation and constraints handling 30/48
40 FSP and PID with plant constraints In real process control action is limited, as well as slew rate Also, process output should be between limits Anti-windup (AW) can be used to mitigate the effect of the saturation in the integral action in PID and FSP MPC appears as a direct solution to implement optimal control under system constraints When is MPC a better choice? PID control of dead-time processes: robustness, dead-time compensation and constraints handling 31/48
41 MPC, FSP and PID GPC Generalized predictive controller
42 GPC analysis for Dead-time Processes General MPC idea control action Process Plant output control action Model Model output (future values) Control Computation Min J(u) Reference Constraints PID control of dead-time processes: robustness, dead-time compensation and constraints handling 33/48
43 GPC analysis for Dead-time Processes General MPC idea control action Process Plant output control action Model Model output (future values) GPC cost Control Computation Min J(u) Reference Constraints GPC Model PID control of dead-time processes: robustness, dead-time compensation and constraints handling 33/48
44 GPC analysis for Dead-time Processes General MPC idea control action Process Plant output control action Model Model output (future values) GPC cost TUNNING Control Computation Min J(u) Reference Constraints GPC Model PID control of dead-time processes: robustness, dead-time compensation and constraints handling 33/48
45 GPC analysis for Dead-time Processes General MPC idea control action Process Plant output control action Model Model output (future values) GPC cost TUNNING Control Computation Min J(u) Reference Constraints GPC Model PID control of dead-time processes: robustness, dead-time compensation and constraints handling 33/48
46 GPC analysis for Dead-time Processes General MPC idea control action Process Plant output control action Model Model output (future values) GPC cost TUNNING Control Computation Min J(u) Reference Constraints GPC Model PID control of dead-time processes: robustness, dead-time compensation and constraints handling 33/48
47 Prediction computation GPC analysis for Dead-time Processes past Delay horizon Prediction horizon of J k kd kd1 kdn PID control of dead-time processes: robustness, dead-time compensation and constraints handling 34/48
48 Prediction computation GPC analysis for Dead-time Processes past Delay horizon Prediction horizon of J GPC structure? k kd kd1 kdn PID control of dead-time processes: robustness, dead-time compensation and constraints handling 34/48
49 Prediction computation GPC analysis for Dead-time Processes past Delay horizon Prediction horizon of J k kd kd1 kdn GPC structure? w(k) q(k) (unconstrained) F(z) C(z) P(z) _ y(k) G n (z) y p (k) optimal predictor z -d F r (z) _ PID control of dead-time processes: robustness, dead-time compensation and constraints handling 34/48
50 Prediction computation GPC analysis for Dead-time Processes past Delay horizon Prediction horizon of J k kd kd1 kdn GPC structure? w(k) q(k) (unconstrained) F(z) C(z) P(z) _ y(k) G n (z) y p (k) optimal predictor z -d F r (z) _ PID control of dead-time processes: robustness, dead-time compensation and constraints handling 34/48
51 GPC analysis for Dead-time Processes Unconstrained GPC structure GPC is equivalent to a discrete FSP FSP can be tuned using GPC method (exactly the same solution) FSP-MPC can be used (for robust controllers and easy tuning)* For 1 st order models GPC 2DOF FSP (PI primary controller) Comparison FSP-PID is valid for GPC-PID for 1 st order models Is valid for other linear MPC (simply a model rearrangement) Constrained case? * Normey-Rico and Camacho, 2007, Springer * Lima, Santos and Normey-Rico, 2015, ISA Transactions PID control of dead-time processes: robustness, dead-time compensation and constraints handling 35/48
52 GPC for dead-time processes Constrained GPC w(k) Optimization constraints u(k) q(k) Process y(k) y p (k) Predictor PID control of dead-time processes: robustness, dead-time compensation and constraints handling 36/48
53 GPC for dead-time processes Constrained GPC w(k) Optimization constraints u(k) q(k) Process y(k) y p (k) Predictor PID control of dead-time processes: robustness, dead-time compensation and constraints handling 36/48
54 GPC for dead-time processes Constrained GPC w(k) Optimization constraints u(k) q(k) Process y(k) y p (k) Predictor All constraints are written as a linear inequality on u PID control of dead-time processes: robustness, dead-time compensation and constraints handling 36/48
55 GPC for dead-time processes Constrained GPC w(k) Optimization constraints u(k) q(k) Process y(k) y p (k) Predictor All constraints are written as a linear inequality on u QP solved at each sample time Only u(k) is applied The horizon window is displaced PID control of dead-time processes: robustness, dead-time compensation and constraints handling 36/48
56 GPC for dead-time processes Constrained GPC w(k) Optimization constraints u(k) q(k) Process y(k) y p (k) Predictor All constraints are written as a linear inequality on u QP solved at each sample time Only u(k) is applied The horizon window is displaced GPC gives goods results with small Nu (in many applications Nu=1 is enough*) * De Keyser and Ionescu, IEEE CCA 2003 PID control of dead-time processes: robustness, dead-time compensation and constraints handling 36/48
57 AW scheme AW for FSP and PID Valid for PID and FSP AW originally derived for control action constraints Several AW strategies in literature PID control of dead-time processes: robustness, dead-time compensation and constraints handling 37/48
58 AWP with error recalculation (ER) Recalculation of the error signal at every sample Objective: to maintain the consistence between u(k) (computed) and ur(k) (applied) * Flesch and Normey-Rico, Control Eng. Practice, 2017 *Silva, Flesch and Normey-Rico, IFAC PID 18 PID control of dead-time processes: robustness, dead-time compensation and constraints handling 38/48
59 AWP with error recalculation (ER) Recalculation of the error signal at every sample Objective: to maintain the consistence between u(k) (computed) and ur(k) (applied) PID case * Flesch and Normey-Rico, Control Eng. Practice, 2017 *Silva, Flesch and Normey-Rico, IFAC PID 18 PID control of dead-time processes: robustness, dead-time compensation and constraints handling 38/48
60 AWP with error recalculation (ER) Recalculation of the error signal at every sample Objective: to maintain the consistence between u(k) (computed) and ur(k) (applied) PID case Consider:? * Flesch and Normey-Rico, Control Eng. Practice, 2017 *Silva, Flesch and Normey-Rico, IFAC PID 18 PID control of dead-time processes: robustness, dead-time compensation and constraints handling 38/48
61 AWP with error recalculation (ER) Recalculation of the error signal at every sample Objective: to maintain the consistence between u(k) (computed) and ur(k) (applied) PID case Consider:? Used in the code to update the error: e(k-1)=e*(k) * Flesch and Normey-Rico, Control Eng. Practice, 2017 *Silva, Flesch and Normey-Rico, IFAC PID 18 PID control of dead-time processes: robustness, dead-time compensation and constraints handling 38/48
62 AWP with error recalculation (ER) Recalculation of the error signal at every sample Objective: to maintain the consistence between u(k) (computed) and ur(k) (applied) PID case Consider:? Used in the code to update the error: e(k-1)=e*(k) ER* better results, principally in noise environment * Flesch and Normey-Rico, Control Eng. Practice, 2017 *Silva, Flesch and Normey-Rico, IFAC PID 18 PID control of dead-time processes: robustness, dead-time compensation and constraints handling 38/48
63 AW for dead-time processes Including several constraints in AW scheme PID control of dead-time processes: robustness, dead-time compensation and constraints handling 39/48
64 AW for dead-time processes Including several constraints in AW scheme Direct PID control of dead-time processes: robustness, dead-time compensation and constraints handling 39/48
65 AW for dead-time processes Including several constraints in AW scheme Direct PID control of dead-time processes: robustness, dead-time compensation and constraints handling 39/48
66 AW for dead-time processes Including several constraints in AW scheme Direct Using prediction ideas MODEL Predictions PID control of dead-time processes: robustness, dead-time compensation and constraints handling 39/48
67 AW for dead-time processes Including several constraints in AW scheme Direct Using prediction ideas MODEL Predictions PID control of dead-time processes: robustness, dead-time compensation and constraints handling 39/48
68 AW for dead-time processes SIMPLE CASE: FOPDT PID control of dead-time processes: robustness, dead-time compensation and constraints handling 40/48
69 AW for dead-time processes SIMPLE CASE: FOPDT PID control of dead-time processes: robustness, dead-time compensation and constraints handling 40/48
70 AW for dead-time processes SIMPLE CASE: FOPDT PID control of dead-time processes: robustness, dead-time compensation and constraints handling 40/48
71 AW for dead-time processes SIMPLE CASE: FOPDT PID control of dead-time processes: robustness, dead-time compensation and constraints handling 40/48
72 AW for dead-time processes SIMPLE CASE: FOPDT PID control of dead-time processes: robustness, dead-time compensation and constraints handling 40/48
73 GPC or FSP(PID) ER-AW? Constrained GPC or FSP-ER-AW Good tuned FSP with ER-AWP equivalent to GPC (Nu=1) On-line optimization is avoided with FSP FSP filter tuning is easy in practice Several successful applications in solar systems and refrigeration plants * In robust industrial solutions PID-ER-AW Simple models are used Robust tuning (low Ms or high Rm values) * Roca, Guzman, Normey-Rico, Berenguel and Yebra, Solar Energy, 2011 * Flesch and Normey-Rico, Control Eng. Practice, 2017 PID control of dead-time processes: robustness, dead-time compensation and constraints handling 41/48
74 Water temperature control Experiments: Electrical water heater heater TRIAC control Process variable Normalized Control variable (number pulses) Model identification: step test Same IAE performance PID smother control action PID control of dead-time processes: robustness, dead-time compensation and constraints handling 42/48
75 Temperature control New GPC tuning to accelerate the responses Problems: Small performance improvement Lower robustness Lower noise attenuation PID is simpler and better PID control of dead-time processes: robustness, dead-time compensation and constraints handling 43/48
76 Compressor-test system vapor conditions PID control of dead-time processes: robustness, dead-time compensation and constraints handling 44/48
77 Compressor-test system vapor conditions Important To maintain Inlet temperature Fast set-point response Fast disturbance rejection Delay error well estimated PID control of dead-time processes: robustness, dead-time compensation and constraints handling 44/48
78 Compressor-test system vapor conditions Important To maintain Inlet temperature Fast set-point response Fast disturbance rejection Delay error well estimated FSP ER-AWP PID control of dead-time processes: robustness, dead-time compensation and constraints handling 44/48
79 Compressor-test system fast disturbances vapor conditions artificial Important To maintain Inlet temperature Fast set-point response Fast disturbance rejection Delay error well estimated FSP ER-AWP PID control of dead-time processes: robustness, dead-time compensation and constraints handling 44/48
80
81 When controlling dead-time processes... Performance measurement after the dead-time Ideal solution can be achieved by FSP (or other improved DTC) Dead-time estimation error is very important Constrained case: ER AW FSP can be equivalent to MPC PID for dead-time processes Can be tuned as a low order approximation of FSP Performance improvement is limited in complex cases For high robust solutions PID is equivalent to FSP (even for high L) ER AW PID sub-optimal solution with good results. PID control of dead-time processes: robustness, dead-time compensation and constraints handling 46/48
82 When controlling dead-time processes... Performance measurement after the dead-time Ideal solution can be achieved by FSP (or other improved DTC) Dead-time estimation error is very important Constrained case: ER AW FSP can be equivalent to MPC PID for dead-time processes Can be tuned as a low order approximation of FSP Performance improvement is limited in complex cases For high robust solutions PID is equivalent to FSP (even for high L) ER AW PID sub-optimal solution with good results. Low-order-process models Large modelling error Noise environment Typical constraints Well tuned robust PID with AW is the best option PID control of dead-time processes: robustness, dead-time compensation and constraints handling 46/48
83 PID still has an important figure in process industry DTC strategies with PI or PID primary controllers can be considered as extensions of simple PID control and used in particular cases Improved AW PID algorithms (or FSP AW) can be the solution in modern real-time distributed control systems for simple constrained systems MPC solutions are important in complex well modeled systems and at second level control PID control of dead-time processes: robustness, dead-time compensation and constraints handling 47/48
84 Thanks! For your attention PID18 organizers UFSC PID control of dead-time processes: robustness, dead-time compensation and constraints handling 48/48
85 UFSC DYCOPS Dycops 2019 /48
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