Robust iterative learning control for Takagi-Sugeno fuzzy nonlinear systems via preview control

Preview control utilizes future reference signal information to enhance system dynamic response and tracking performance. Iterative learning control represents an effective approach for managing repetitive tasks and has found widespread applications in industrial systems. This paper presents an innovative methodology for developing fuzzy iterative learning preview control using the Takagi-Sugeno (T-S) fuzzy model is discussed. The design incorporates robustness against time-varying uncertainties, and reference tracking capabilities. To accomplish this objective, the T-S fuzzy system is integrated with time-variant uncertainties to establish an augmented error system, thereby transforming the original fuzzy iterative learning preview control problem issue into a stability analysis of the augmented error systems. Subsequently, two distinct fuzzy iterative learning preview control laws are formulated by incorporating the T-S fuzzy system's states or outputs, tracking error, and a previewed reference signal to address the tracking control challenge. Novel sufficient conditions for the asymptotic stability of the augmented error system are derived using the linear matrix inequality (LMI) technique and fuzzy Lyapunov function analysis. Finally, the effectiveness of both proposed control strategies is validated through two numerical examples.