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

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, especially when hard constraints are taken into account, due to the complex numerical optimization problem that has to be solved at every sampling time. The 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 a simulation example are given in the paper.