Model predictive control for uncertain max–min-plus-scaling systems

摘要: In this paper we extend the classical min–max model predictive control framework to a class of uncertain discrete event systems that can be modelled using operations maximization, minimization, addition and scalar multiplication, call max–min-plus-scaling (MMPS) systems. Provided stage cost is an MMPS expression considering only linear input constraints then open-loop problem for transformed into sequence programming problems. Hence, solved efficiently, despite fact system non-linear. A feedback approach disturbance policies also presented, which leads improved performance compared approach.