An approach robust nonlinear model predictive control with state-dependent disturbances via linear matrix inequalities

Abstract

The issue of nonlinear model predictive control has always been a topic of much concern. We will propose a new approach to robust nonlinear model predictive control to class of nonlinear model system with input constraint under state-dependent disturbances. The considered class of model is separated into linear part at current state, nonlinear part and state-dependent disturbances which are assumed to have their bound. The state-feedback control law is obtained by that solving optimization problem of upper bound of infinite horizon cost function with input constraint via LMIs. In this paper, in order to guarantee robust stability, the proposed approach must generates feasible regions which ensures the existence of a solution and stable region bounded by that. Moreover, these regions are able to contract after every sampling time to proof the robust stability of the system. The simulation results demonstrate the good performance of the proposed approach to RNMPC.