Predictive control for general nonlinear systems using approximation

This paper addresses a tracking problem for general nonlinear systems using model predictive control (MPC). After approximating 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. Different from other nonlinear model predictive control (NMPC), there is a built-in integral action in the developed scheme and the implementation issues are discussed. Further more, it is pointed out that the proposed NMPC derived using approximation can stablise the original nonlinear systems if certain condition, which can be met by properly choosing predictive times and the order for Taylor expansion, is satisfied. Simulation demonstrates the effectiveness of the proposed NMPC.