Efficient implementation of min-max model predictive control with bounded uncertainties

• Published in: 2002 IEEE International Conference on Control Applications Vol. 2; pp. 651 - 656 vol.2
• Main Authors: Alamo, T., Ramirez, D.R., Camacho, E.F.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2002
• Subjects: Approximation error; Contracts; Costs; Equations; Mathematical model; Neural networks; Predictive control; Predictive models; Sampling methods; Uncertainty
• ISBN: 0780373863; 9780780373860
• DOI: 10.1109/CCA.2002.1038677

Min-Max Model Predictive Control (MMMPC) is one of the strategies used to control plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the complex numerical optimization problem that has to be solved at every sampling time. This paper shows how to overcome this by transforming the original problem into a reduced min-max problem whose solution is much simpler. In this way, the range of processes to which MMMPC can be applied is considerably broadened. Proofs based on the properties of the cost function and simulation examples are given in the paper.