Model Predictive Control: On-line optimization versus explicit precomputed controller

1. Model Predictive Control: On-line optimization versus explicitprecomputed controller Espen Storkaas Trondheim, 7.6. 2005

2. Outline • Introduction • Brief history • Linear MPC • Theory, feasibility, stability, performance • Derivation of explicit MPC • Nonlinear and hybrid MPC • Applications • Future directions • Conclusions

3. Introduction Control problem: Find stabilizing control strategy that • Minimize objective functional • Satisfies constraints • is robust towards uncertainty

4. Closed loop optimal control Feedback: u=k(x) s.t. closed loop trajectories satisfying optimality Advantages: Feedback Uncertainty Disturbances Unstable systems Drawbacks Find k(x)? Open loop optimal control Input trajectory: u=u(t,x0) solving optimization problem Advantages: Computationally feasible Drawbacks: No feedback Disturbances? Unstable systems Uncertainty Solution strategies

5. Possible solution 1 : MPC with online optimization • Solve optimization problem over finite horizon • Implement optimal input for 2[t,t+d] • Re-optimize at next sample (feedback) • Optimal control inputs implicitly via optimalization

6. MPC with online optimization (Allgöwer, 2004)

7. Close loop optimal control Feedback: u=k(x) s.t. closed loop trajectories satisfying optimality Advantages: Feedback Uncertainty Disturbances Unstable systems Drawbacks Find k(x)? Open loop optimal control Input trajectory: u=u(t,x0) solving optimization problem Advantages: Computationally feasible Drawbacks: No feedback Disturbances? Unstable systems Uncertainty Solution strategies

8. Possible solution 2: Explicit MPC(Bemporad et al., 2002, Tøndel et al., 2003) • Solve optimization problem offline for all x2X • For linear systems: multiparametric QP (mp-QP) with solution • Piecewise affine controller • Exactly identical to implicit solution (via online optimization)

9. Model Predictive Control (MPC)Brief history(Qin & Badgwell, 2003) • LQR (Kalman, 1964) • Unconstrained infinite horizon • Constrained finite horizon – MPC (Richalet et al., 1978, Cutler & Ramaker,1979) • Driven by demands in industry • Defined MPC paradigm • Posed as quadration program (QP) (Cutler et al. 1983) • Constraints appear explicitly • Academic research (919 papers in 2002! (Allgöwer, 2004)) • Stability • Performance • Explicit MPC (Bemporad et al. 2002, Tøndel et al. 2003)

10. Linear MPC – Problem formulation(Scokaert & Rawlings, 1998, Bemporad et al, 2002) • Linear time-invariant discrete model: • Objective: • Constraints:

11. Linear MPC – Unconstrained case • Problem: • Classical LQR solution (Kalman 1960) • K calculated from algebraic Ricatti equation • Assymptotically stabilizing

12. Linear MPC – Infinite horizon (Constrained LQR) • Problem: • Infinite number of decision variables • Stability proved by Rawlings & Muske (1993) • Computationally feasible (Scokeart & Rawlings, 1998) • Computationally expensive

13. Linear MPC – Finite input horizon • Problem: • Achieved solution: • Stabilizing for K=0 and K=KLQ provided N large enough

14. Important aspects x(t+) * x(t) * • Feasibility • Slack on output constraints • Feasible region for unstable systems under input constraints • Closed loop stability • Contraction constraint • Terminal constraint (x(k+N)=0) • Stable for control horizon N ”large enough” • Performance • Implemented control trajectory may differ significantly from computed open-loop optimal • May lead to infeasibility • Solution: Long enough control horizon • On-line computational requirements

15. Derivation of explicit MPC(Bemporad et al., 2002) • Rewrite constrained LQR problem: • QP parameterized in initial state x(t) • Solution for all x(t) by multi-parametric quadratic program (mp-QP) • Solve mp-QP offline to find optimal solution U*t=U*(x(t)) • Optimal input given by

16. Derivation of explicit MPC (2) • With • From Karush-Kuhn-Tucker optimality conditions and assuming linearly independent active constraints: • KKT conditions gives partitioning of feasible regions into polyhedra • Inherits properties of optimization problem

17. Partitioning of state spaceOffline computations Typical Algorithm: • Choose initial active set • Find control law for active set • Find critical region correspond to active set • Systematic exploration of remaining parameter space • (Build search tree/reduce complexity) Bemproad et al. 2002 Tøndel et al. 2003

18. Determine critical region Sequential search Binary search tree Implement optimal control Complexity of partition increses with # states/parameters Explicit MPC:Online computations Binary search tree Sequential search

19. Properties of explicit MPC • Dimensional explosion • max 5-7 states/parameter with current formulation • Disturbance rejection, reference tracking and soft/variable constraints can be included, but increases complexity • Greatly simplified code vs. online optimization • Safety-critical systems

20. Nonlinear MPC • Based on nonlinear process model and/or constraints to improve forcasting • Requires solution of NLP, generally non-convex • Stability and performance issues more important • ”There are no analysis methods available that permit to analyze close loop stability based on knowledge of plant model, objective functional and horizon lengths” (Allgöwer et al.,1999) • Approaches: • Infinite horizon NMPC • Zero state terminal equality constraint • Dual mode NMPC • Contractive NMPC • Quasi-infinite horizon NMPC

21. Nonlinear explicit MPC • Exact solution cannot be represented as PWA control law • Approximative PWA solutions with user-specified tolerance can be found (Johansen, 2004) • Solution of NLP’s offline • k-d tree partitioning of state space • Joint convexity of obejctive functional and constraints assumed • Complexity similar to linear explicit MCP • Guaranteed stability under assumptions on tolerance • Larger potential than linear EMPC?

22. Hybrid MPC • Applications to broad class of systems including • Linear hybrid dynamical systems • Piecewise linear systems (including approximations of nonlinear systems • Linear systems with constraints • Modeled as mixed logical dynamical systems (Bemporad & Morari, 1999) • MPC problem is MILP/MIQP • Difficult to solve online in available time • Explicit Hybrid MPC is PWA (Bemporad et al. 2002, Dua et al. 2002) • Identical to implicit solution found by online optimization

23. Application areas

24. Future directions • Linear MPC • Improved models / adaptive formulations • Multi-objective, prioritized constraints etc. • Nonlinear/Hybrid MPC • Computational efficiency • Guaranteed stability/performance • Explicit MPC • Reduction of complexity vs degree of suboptimality • Reconfigurability • Exploit structure of problem

25. Concluding remarks • Online optimization MPC for • Slow systems • Large systems • Explicit precomputed MPC for • Small systems with high sampling rate • Safety critical • Dedicated hardware (controller on a chip) Acknowledgements Thanks to Tor Arne Johansen, Petter Tøndel and Olav Slupphaug for invaluable help with preparing this presentation

26. Selected References Allgöwer, F. (2004), Model Predictive Control: A Success Story Continues, APACT’04, Bath,April 26-28, 2004 Allgöwer, F., Badgwell, T.A., Qin, S.J., Rawlings, J.B. and Wright, S.J., (1999). Nonlinear predictive control and moving horizon estimation—an introductory overview. In: Frank, P.M., Editor, , 1999. Advances in control: highlights of ECC ’99, Springer, Berlin. Bemporad, A., Morari, M., Dua, V. and Pistikopoulos, E.N. (2002), The explicit linear quadratic regulator for constrained systems. Automatica38 1, pp. 3–20, 2002. Bemporad A, Borrelli F, Morari M, (2002). On the optimal control law for linear discrete time hybrid systems, Lecture notes in computer science 2289: 105-119 2002 Bemporad A, Morari M, (1999), Control of systems integrating logic, dynamics and constraints, Automatica 35 (3): 407-427 MAR 1999 Cutler, C. R., & Ramaker, B. L. (1979). Dynamic matrix control—a computer control algorithm. AICHE national meeting, Houston, TX, April 1979. Cutler, C., Morshedi, A., & Haydel, J. (1983). An industrial perspective on advanced control. In AICHE annual meeting, Washington, DC, October 1983 Dua V, Bozinis NA, Pistikopoulos EN. (2002), A multiparametric approach for mixed-integer quadratic engineering problems, Computers & Chemical Engineering 26 (4-5): 715-733 MAY 15 2002

27. Selected References Kalman, R. (1964), When is a linear control system optimal?, Journal of Basic Engineering – Transactions on ASME – Series D, 51-60, Johansen, T.A., Approximate Explicit Receding Horizon Control of Constrained Nonlinear Systems, Automatica, Vol. 40, pp. 293-300, 2004 Qin, SJ., Badgwell, TA., A survey of industrial model predictive control technology, Control Engineering practice 11 (7): 733-764, 2003 Rawlings, J.B. and Muske, K.R., 1993. Stability of constrained receding horizon control. IEEE Transactions on Automatic Control38 10, pp. 1512–1516 Richalet, J., Rault, A., Testud, J.L. and Papon, J., Model predictive heuristic control: Applications to industrial processes. Automatica14, pp. 413–428, 1978 Scokaert, P.O.M. and Rawlings, J.B., Constrained linear quadratic regulation. IEEE Transactions on Automatic Control43 8, pp. 1163–1169, 1998 Tøndel, P., Johansen, T.A. and Bemporad, A.(2003), An algorithm for multi-parametric quadratic programming and explicit MPC solutions. Automatica39, 2003 Tøndel, P., Johansen, T.A. and Bemporad, A (2003). Evalution of piecewise affine control via binary search tree. Automatica39, 2003

28. Ting som ikke er nevnt • Robusthet • Practical implementations

29. Thank you for your attention!

30. Functional spec. in modern MPC • Prevent violation of input and output constraints • Drive CV’s to steady state optimal values (or within bounds) • Drive MV’s to steady state optimal values (or within bounds) • Prevent excessive use of MVs • In case of signal or actuator failure, control as much of the plant as possible as possible

31. Modern industrial MPC algorithmOverview • Read MV, CV, DV • Output feedback • Determination of controlled sub-process • Removal of ill-condisioned plant • Local steady state optimization • Dynamical optimization • MV’s to process

32. Modern industrial MPC algorithmOutput feedback • Process states and kalman filter seldom used • Ad-hoc biasing scheemes with challenges regarding • Extra measurements ? • Linear combinations of states? • Unmeasured disturbances models? • Measurements noise? • Implications • Sluggish input disturbance rejection • Poor control of integrating and unstable systems

33. Modern industrial MPC algorithmDynamic optimization Deviations from output trajectory Output slack variables Input deviations Input moves Process model Output constraints Input constraints

34. Modern industrial MPC algorithmDynamic optimization (2) • Solved as a sequence according to prioritized constraints and targets • Hard constraint on MV rate of change (always) • Hard constraint on MV magnitude • Sequential high priority soft constraints on CV’s • Set point control • Sequencial low priority soft constraints on CV’s and MV’s

35. Limitations with modern MPC algorithms

36. Pros/Cons

37. Road Ahead

38. Plan • Introduction • General control problem formulation • Goal • Constraints-ARW or MPC • Uncertainty • Etc. • control hierachy • MPC • History • Drivers (industry, academia) • Development • State of the art • Theorethical status • Fuctionality • Industrial Practice • Limitations • Theory • Explicit MPC • History • Drivers • Theory • State of the art • Practical implementations? • limitations • Pros/cons Online opt./xplicit • Future • What drives the development? • Explicit MPC in process industry? Which problems can this solve? • Other industries? Probably skip! • Can challenges with explicit MPC be resolved faster than growth in computing power needed for online opt • Robustness of online opt

39. Optimal operation of constrained processes Control of exothermal reaction • Maximize throughput • Quality requirements • Limited cooling capacity • Variable feed composition and temperature