Model predictive control for nonlinear systems with time-varying dynamics and guaranteed Lyapunov stability

This article focuses on model predictive control (MPC) of nonlinear systems in the case that the system parameters are inaccurate due to equipment wear or environmental changes. An MPC where the parameters of the predictive model are recursive estimated is proposed for nonlinear continuous time systems. The range of initial state that is able to guarantee the state always bounded in an allowable stability region, even when there does not exist any robust control law designed based on the mismatched initial model, is deduced. The corresponding optimization problem is designed based on Lyapunov controller techniques and includes parameter estimation parts. By this method, the state will eventually converge to a small neighborhood of the desired set-point. Stability analysis is performed and an application of the proposed method to the chemical process is presented to show the effectiveness of the proposed method.