Two-dimensional iterative learning fault-tolerant control for batch processes via a high-order fully actuated...

With the increasing complexity of equipment, faults in batch processes have become increasingly unavoidable. Conventional iterative learning fault-tolerant control methods, which rely solely on enhancing robustness, are often insufficient to meet the system's tracking accuracy requirements. To address this challenge, a two-dimensional iterative learning fault-tolerant control strategy is developed for batch processes subject to disturbances and actuator faults, based on the high-order fully actuated framework. Different from the observer-controller co-design strategies, the developed approach structurally separates the design of the observer and controller, thereby eliminating the negative impact of gain coupling on estimation accuracy. Moreover, historical control inputs are incorporated into the control law design to effectively remove historical input terms from the closed-loop system, significantly reducing system dimensionality and computational burden. Finally, the simulation results of the batch stirred reactor show that the root mean square error, integral absolute error, and average dynamic tracking indicator of the developed method with compensation (the 10th cycle) are 0.6801, 0.6207, and 391.05, respectively, which are significantly reduced compared with the case without compensation, indicating that the developed method achieves superior tracking performance.