Two-dimensional iterative learning control with deep reinforcement learning compensation for the non-repetitive uncertain batch processes

Abstract

Iterative learning control (ILC) is an advantage control strategy widely used in batch systems. Nevertheless, designing an effective iterative learning control scheme remains crucial for complex batch systems with model mismatch and non-repetitive nature. In this paper, we propose a two-dimensional iterative learning control-reinforcement learning (2D ILC-RL) control scheme composed of a two-dimensional ILC controller and a two-dimensional DRL compensator. Based on the 2D system theory, the 2D ILC controller is proposed to ensure the primary control performance and its stability and convergence are verified. Meanwhile, the DRL compensator counteracts the negative impact of the model mismatch and the non-repetitive nature. In addition, we proposed a real-time implementation scheme to guarantee the safety of the practical batch systems compared to the conventional online training method. Finally, the simulation results in the injection molding batch process and the nonlinear continuously stirred tank reactor demonstrate the proposed control scheme’s effectiveness, significant control performance, and strong robustness.