Improved control using extended non-minimal state space MPC and modified LQR for a kind of nonlinear systems

Inspired by the state space model based predictive control, this paper presents the combination design of extended non-minimal state space predictive control (ENMSSPC) and modified linear quadratic regulator (LQR) for a kind of nonlinear process with output feedback coupling, which shows improved control performance for both model/plant match and model/plant mismatch cases. In many previous control methods for this kind of nonlinear systems, the nonlinear part is treated in different ways such as ignored, represented as a rough linear one or assumed to be time-variant when corresponding predictive control methods are designed. However, the above methods will generally lead to information loss, resulting in the influenced control performance. This paper will show that the ENMSSPC-LQ control structure will further improve closed-loop control performance concerning tracking ability and disturbance rejection compared with previous predictive control methods.