Cooperative H Robust Move Blocking Fuzzy Model Predictive Control of Nonlinear Systems.

The main aim of this article is to provide a systematic move blocking (MB)-based robust model predictive control (MPC) for nonlinear systems due to the model uncertainties and disturbances based on Takagi–Sugeno fuzzy models. The suggested robust MPC (RMPC) consists of an offline H∞ fuzzy controller (OHFC) and an online MB-based MPC. In the first step, by considering a nonquadratic Lyapunov function (NQLF), a new one-step linear matrix inequality (OSLMI) problem is proposed to guarantee robust tracking performance. Then, the provided OHFC is considered in the design procedure of the online MB-based MPC to calculate the overall control signal. So, the MB-based MPC is developed based on a prerobustly stabilized system. This means that the online part focuses on the optimality of the overall control law in a constrained scheme. The proposed Lyapunov function of the OHFC and an ellipsoidal terminal constraint (ETC) are utilized as the terminal cost and terminal set in the design process of the MB-based MPC to improve the feasibility of the online optimization problem (OP). Since the online OP is solved due to the prerobustly stabilized system and the MB scheme, so, the online computational burden is significantly reduced. In summary, the main objective of this article is to propose an RMPC synthetized with an offline $H_{\infty }$ controller to satisfy the system constraints and guaranteeing the robust and optimal performance with a low computational complexity. A numerical example and a truck–trailer system (TTS) are simulated to illustrate the superiority and conservatism reduction of the proposed MB-based RMPC.