Model Predictive Control

Model Predictive Control (MPC) is widely adopted in industry as an effective means to deal with large multivariable constrained control problems. The main idea of MPC is to choose the control action by repeatedly solving on line an optimal control problem. This aims at minimizing a performance criterion over a future horizon, possibly subject to constraints on the manipulated inputs and outputs, where the future behavior is computed according to a model of the plant. Issues arise for guaranteeing closed-loop stability, to handle model uncertainty, and to reduce on-line computations.

Main publications

[1]   A. Bemporad, L. Chisci, and E. Mosca, “On the stabilizing property of SIORHC,” Automatica, vol. 30, no. 12, pp. 2013–2015, 1994.
[2]   A. Bemporad, A. Casavola, and E. Mosca, “Nonlinear control of constrained linear systems via predictive reference management,” IEEE Trans. Automatic Control, vol. AC-42, no. 3, pp. 340–349, 1997.
[3]   A. Bemporad, “Reference governor for constrained nonlinear systems,” IEEE Trans. Automatic Control, vol. AC-43, no. 3, pp. 415–419, 1998.
[4]   A. Bemporad, “A predictive controller with artificial Lyapunov function for linear systems with input/state constraints,” Automatica, vol. 34, no. 10, pp. 1255–1260, 1998.
[5]   A. Bemporad, “Model-based predictive control design: New trends and tools,” in Proc. 45th IEEE Conf. on Decision and Control, San Diego, CA, 2006, pp. 6678–6683.
[6]   A. Bemporad, “Reducing conservativeness in predictive control of constrained systems with disturbances,” in Proc. 37th IEEE Conf. on Decision and Control, Tampa, FL, 1998, pp. 1384–1391.

Tools

[7] A. Bemporad, M. Morari, and N. L. Ricker, Model Predictive Control Toolbox for Matlab, The Mathworks, Inc., 2004.
[8] A. Bemporad, Hybrid Toolbox for Matlab, 2003-2007.

Other relevant publications

[9]   A. Bemporad, “Predictive control of teleoperated constrained systems with unbounded communication delays,” in Proc. 37th IEEE Conf. on Decision and Control, Tampa, FL, 1998, pp. 2133–2138.
[10]   G. Pannocchia and A. Bemporad, “Combined design of disturbance model and observer for offset-free model predictive control,” IEEE Trans. Automatic Control, vol. 52, no. 6, pp. 1048–1053, 2007.
[11]   D. Muñoz de la Peña, T. Alamo, A. Bemporad, and E.F. Camacho, “A decomposition algorithm for feedback min-max model predictive control,” IEEE Trans. Automatic Control, vol. 51, no. 10, pp. 1688–1692, 2006.
[12]   A. Bemporad, W.P.M.H. Heemels, and B. De Schutter, “On hybrid systems and closed-loop MPC systems,” IEEE Trans. Automatic Control, vol. 47, no. 5, pp. 863–869, May 2002.
[13]   A. Bemporad and A. Garulli, “Output-feedback predictive control of constrained linear systems with disturbances via set-membership state estimation,” International Journal of Control, vol. 73, no. 8, pp. 655–665, 2000.
[14]   A. Bemporad and E. Mosca, “Fulfilling hard constraints in uncertain linear systems by reference managing,” Automatica, vol. 34, no. 4, pp. 451–461, 1998.
[15]   A. Bemporad and M. Morari, “Robust model predictive control: A survey,” in Robustness in Identification and Control, A. Garulli, A. Tesi, and A. Vicino, Eds., number 245 in Lecture Notes in Control and Information Sciences, pp. 207–226. Springer-Verlag, 1999.