Output feedback based robust iterative learning control via a heuristic approach for batch processes with time-varying state delays and uncertainties. (August 2022) - Wei, Junyu; Tao, Hongfeng; Hao, Shoulin; Paszke, Wojciech; Gałkowski, Krzysztof
Abstract: This paper proposes an output feedback (OF) based PD-type robust iterative learning control (ILC) scheme for a class of discrete batch processes with time-varying delays and uncertainties in the absence of accurate state measurements, which is convenient for practical applications. Based on a repetitive process model and Lyapunov–Krasovskii theory, sufficient conditions are established in terms of linear matrix inequalities (LMIs) to ensure the robust stability along the trial for all admissible uncertainties. A two-stage heuristic approach is developed to iteratively calculate the feasible ILC gains based on a pre-designed state feedback (SF) controller, which circumvents the non-convex stability conditions expressed in bilinear matrix inequalities caused by OF. In addition, the conditions can be extended to the case of non-repetitive uncertainties in the system and meet the prescribed H ∞ performance level. Finally, a simulation of injection molding process is given to demonstrate the effectiveness of the proposed method. Highlights: PD-type iterative learning control (ILC) for batch processes with time-varying delays. The robust H ∞ performance index is considered under non-repetitive uncertainties. Robust ILC updating law based on linear repetitive process model setting. Using output information is beneficial to practical implementation. The heuristic algorithm circumvents the non-convex stability conditions.