Predictive control of general nonlinear systems using approximation

The paper addresses a tracking problem for general nonlinear systems using model predictive control (MPC). After approximation of the tracking error in the receding horizon by its Taylor-series expansion to any specified order, an analytic solution to the MPC is developed and a closed-form nonlinear predictive controller is presented. Unlike other nonlinear-model predictive control (NMPC), there is a built-in integral action in the developed scheme and the implementation issues are discussed. It is pointed out that the proposed NMPC derived using approximation can stabilise the original nonlinear systems if certain conditions, which can be met by properly choosing predictive times and the order for Taylor expansion, are satisfied. Simulation demonstrates the effectiveness of the proposed NMPC.