Robust delay dependent iterative learning fault-tolerant control for batch processes with state delay and actuator failures

Based on a two-dimensional (2D) Fornasini–Marchsini system description of a batch process in industry, a robust state feedback integrated with an iterative learning reliable control (FILRC) scheme is proposed. The scheme is intended for batch processes with uncertain perturbations and state delay subject to actuator failures, which is dependent on the upper and lower delay bounds of the interval time-varying delay. The relevant concepts of fault-tolerance are introduced. The proposed control law can guarantee closed-loop convergence along both time and cycle directions to satisfy H∞ performance even with unknown disturbances and actuator failures. By introducing a new 2D Lyapunov–Krasovskii functional candidate and adding a differential inequality without introducing redundant free-weighting matrices to the difference Lyapunov functional for 2D systems possessing two directions, conditions for the existence of the proposed FILRC scheme are established in terms of linear matrix inequalities (LMIs). By solving these LMIs, the FILRC law is explicitly formulated together with an adjustable robust H∞ performance level. Applications to injection velocity control show that the proposed FILRC achieves the design objectives well.

► A robust state feedback integrated with an iterative learning reliable control (FILRC) scheme is proposed for batch process control.
► Uncertain perturbations and state delay subject to actuator failures are considered.
► The proposed control law can guarantee closed-loop convergence along both time and cycle directions to satisfy H∞ performance.
► Applications to injection velocity control show that the proposed FILRC achieves the design objectives well.