Stable indirect adaptive predictive fuzzy control for industrial processes

The paper proposes a stable indirect adaptive fuzzy predictive control, which is based on a discrete-time Takagi-Sugeno (T-S) fuzzy model and on the Generalized predictive control (GPC) algorithm. The T-S fuzzy model is used to approximate the unknown nonlinear plant, that to provide good accuracy in identification of unknown model parameters, three online adaptive laws are proposed. 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 nonlinear simulated laboratory-scale liquid-level process. The simulation results show that the proposed method has a good performance and disturbance rejection capacity in industrial processes.