Computation algorithms of feasible sets and robust feasible sets for constrained linear time-invariant systems parametrized with orthonormal functions

Document Type : Research Article

Authors

1 Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran

2 Faculty/Amirkabir University of Technology

3 Department of Biomedical Engineering, Amirkabir University of Technology

Abstract

Feasible sets and robust feasible sets have indispensable role in a priori stability guarantee of the constrained systems and in model predictive control. This article presents two algorithms for generating these sets for constrained linear time-invariant systems. Because the conventional algorithms for generating these sets must be applied iteratively over time, they are incapable of dealing with systems having the input vector constructed in any domain other than the time domain. The new algorithms, presented in this article, remove this limitation by treating the input vector monolithically over the time horizon. The presented algorithm for computation of the robust feasible set is capable of incorporating disturbances as well as parametric uncertainties that can be formulated as polytopes. For the verification, the results of the proposed algorithms were compared with results of the conventional methods in a similar circumstance. Finally, examples are presented to compare the computation times of the proposed algorithms with the conventional ones and to illustrate the effect of input vector parametrization, employing orthonormal functions, on the feasible region and the robust feasible region. Results showed that the parametrization improved the feasible set and robust feasible set.

Keywords

Main Subjects


[1] Scibilia Francesco, Sorin Olaru, Morten Hovd. On feasible sets for MPC and their approximations. Automatica 47, no. 1 (2011): 133-139.
[2] Kerrigan, Eric C., Jan M. Maciejowski. Robust feasibility in model predictive control: Necessary and sufficient conditions. In Proceedings of the Decision and Control, 2001, vol. 1, pp. 728-733. IEEE, 2001.
[3] Blanchini, Franco. Survey paper: Set invariance in control. Automatica  35, no. 11 (1999): 1747-1767.
[4] Mayne, David Q., James B. Rawlings, Christopher V. Rao, Pierre OM Scokaert. Constrained model predictive control: Stability and optimality. Automatica 36, no. 6 (2000): 789-814.
[5] Gilbert, Elmer G., K. Tin Tan. Linear systems with state and control constraints: The theory and application of maximal output admissible sets. IEEE Transactions on Automatic control 36, no. 9 (1991): 1008-1020.
[6] Burger, Thomas, Peter Gritzmann, Victor Klee. Polytope projection and projection polytopes. The American mathematical monthly 103, no. 9 (1996): 742-755.
[7] Jones, Colin. Polyhedral tools for control. No. EPFL-THESIS-169769. University of Cambridge, 2005
[8] Kvasnica, Michal, Pascal Grieder, Mato Baotić, and Manfred Morari. "Multi-parametric toolbox (MPT)." In International Workshop on Hybrid Systems: Computation and Control, pp. 448-462. Springer Berlin Heidelberg, 2004.
[9] E.C. Kerrigan, Robust Constraint Satisfaction: Invariant Sets and Predictive Control, PhD thesis, University of Cambridge, Cambridge, 2000.
[10] Kvasnica, Michal, Bálint Takács, Juraj Holaza, and Deepak Ingole. "Reachability Analysis and Control Synthesis for Uncertain Linear Systems in MPT.” IFAC-Papers On Line 48, no. 14 (2015): 302-307.
[11] Bemporad, Alberto, Manfred Morari, Vivek Dua, and Efstratios N. Pistikopoulos. "The explicit linear quadratic regulator for constrained systems." Automatica 38, no. 1 (2002): 3-20.
[12] Zeilinger, Melanie Nicole, Colin Neil Jones, and Manfred Morari. "Real-time suboptimal model predictive control using a combination of explicit MPC and online optimization." IEEE Transactions on Automatic Control 56, no. 7 (2011): 1524-1534.
[13] Wang, Liuping. "Continuous time model predictive control design using orthonormal functions." International Journal of Control 74, no. 16 (2001): 1588-1600.
[14] Wang, Liuping. "Discrete model predictive controller design using Laguerre functions." Journal of process control 14, no. 2 (2004): 131-142.
[15] Khan, Bilal, and J. Anthony Rossiter. "Alternative parameterisation within predictive control: a systematic selection." International Journal of Control 86, no. 8 (2013): 1397-1409.
[16] Rossiter, J. Anthony, Liuping Wang, and Guillermo Valencia-Palomo. "Efficient algorithms for trading off feasibility and performance in predictive control." International Journal of Control 83, no. 4 (2010): 789-797.
[17] Khan, B., G. Valencia‐Palomo, J. A. Rossiter, C. N. Jones, and R. Gondhalekar. "Long horizon input parameterisations to enlarge the region of attraction of MPC." Optimal Control Applications and Methods (2014).
[18] Michalska, Hanna, and David Q. Mayne. "Robust receding horizon control of constrained nonlinear systems." IEEE Transactions on automatic control 38, no. 11 (1993): 1623-1633.
[19] Kerrigan, Eric C., and Jan M. Maciejowski. "Invariant sets for constrained nonlinear discrete-time systems with application to feasibility in model predictive control." In Decision and Control, 2000. Proceedings of the 39th IEEE Conference on, vol. 5, pp. 4951-4956. IEEE, 2000.
[20] Jones, C. N., E. C. Kerrigan, and J. M. Maciejowski. "On polyhedral projection and parametric programming." Journal of Optimization Theory and Applications 138, no. 2 (2008): 207-220.
[21] Blanchini, Franco. "Ultimate boundedness control for uncertain discrete-time systems via set-induced Lyapunov functions." In Decision and Control, Proceedings of the 30th IEEE Conference on, pp. 1755-1760. IEEE, 1991.
[22] Borrelli, F., A. Bemporad, and M. Morari. "Predictive Control for Linear and Hybrid Systems, 2015." preparation, available online at http://www. mpc. berkeley. edu/mpc-course-material pp. 217, (2015).
[23] Oliveira, Gustavo HC, Alex da Rosa, Ricardo JGB Campello, Jeremias B. Machado, and Wagner C. Amaral. "An introduction to models based on Laguerre, Kautz and other related orthonormal functions–part I: linear and uncertain models." International Journal of Modelling, Identification and Control 14, no. 1-2 (2011): 121-132.
[24] Wahlberg, Bo. "Construction and analysis." In Modelling and Identification with Rational Orthogonal Basis Functions, pp. 15-39. Springer London, 2005.
[25] Hemmasian Ettefagh, Massoud, Mahyar Naraghi, Farzad Towhidkhah, and José De Doná. "Model predictive control of linear time varying systems using Laguerre functions." In Control Conference (AuCC), 2016 Australian, pp. 120-125. IEEE, 2016.
[26] Hemmasian Ettefagh, Massoud, José De Doná, Mahyar Naraghi and Farzad Towhidkhah. "Control of constrained linear-time varying systems via Kautz parametrization of model predictive control scheme". In International Conference on Fundamental Research in Electrical Engineering, Iran, July 2017.
[27] Kouvaritakis, Basil, Mark Cannon. Model Predictive Control: Classical, Robust and Stochastic. Springer, 2015.