Fuzzy model predictive control for nonlinear processes

The paper proposes an adaptive fuzzy predictive control method for industrial processes, which is based on the Generalized predictive control (GPC) algorithm. To provide good accuracy in the identification of unknown nonlinear plants, an online adaptive law is proposed to adapt a T-S fuzzy model. It is demonstrated that the tracking error remains bounded. The stability of closed-loop control system is studied and proved via the Lyapunov stability theory. To validate the theoretical developments and to demonstrate the performance of the proposed control, the controller is applied on a simulated laboratory-scale liquid-level process. The simulation results show that the proposed method has good performance and disturbance rejection capacity in industrial processes.