Efficient implementation of constrained min-max model predictive control with bounded uncertainties: a vertex rejection approach

Min-Max Model Predictive Control (MMMPC) is one of the strategies used to control plants subject to bounded additive uncertainties. The implementation of MMMPC suffers a large computational burden due to the NP-hard 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 in which the number of extreme uncertainty realizations to be considered is significantly lowered. Thus, the solution is much simpler. In this way, the range of processes to which MMMPC can be applied is considerably broadened. A simulation example is given in the paper.