Feedback-assisted iterative learning control based on an inverse process model

Abstract A new iterative learning control design in the frequency domain is considered. Intended for application in chemical batch processes, a generic form of a feedback-assisted learning scheme is first considered, and an inverse model-based learning algorithm is derived through convergence analysis in the frequency domain. To enhance robustness to modelling errors and random disturbances, a filtered version is then proposed and tracking performance as well as convergence properties are analysed. Performance of the proposed control method is evaluated through experiments in a bench-scale simulated batch reactor as well as numerical simulations.