HYBRID ITERATIVE LEARNING FAULT-TOLERANT GUARANTEED COST CONTROL DESIGN FOR MULTI-PHASE BATCH PROCESSES

A robust design of a hybrid iterative learning fault-tolerant guaranteed cost control scheme is proposed for a class of multi-phase batch processes under faults and disturbances. Firstly, based on an equivalent two-dimensional Fornasini-Marchesini (2D-FM) switched system with actuator faults varying within an allowable range, a 2D robustly hybrid controller that includes a robust hybrid extended feedback control to ensure performance over time and a hybrid iterative learning control to improve the tracking performance from cycle to cycle is formulated to guarantee the closed-loop convergence and the H1 performance level with a cost function bearing the upper bounds for all admissible uncertainty and actuator failures. Secondly, 2D system theory and the average dwell time strategy are adopted to derive conditions for guaranteeing exponential stability of the corresponding system in terms of linear matrix inequalities (LMIs), where the suboptimal hybrid guaranteed cost controller, which minimizes the quadratic performance index and rejects external disturbances, is designed using a convex optimization under LMI constraints. Finally, the proposed method is further verified by simulation on an injection molding process in comparison with traditional methods.