A reinforcement learning integral sliding mode control scheme against lumped disturbances in hot strip rolling

Abstract

In the hot strip rolling production process, due to the influence of external disturbances and unmodeled errors which are defined as lumped disturbances (LDs), the looper operating angle and strip tension may not satisfy the requirements of normal production under the set values, which may lead to the product not satisfying the requirements. In this paper, an advanced reinforcement learning (RL) algorithm combined with integral sliding mode control (ISMC) is utilized to deal with LDs, and then the robustness of the system is improved. Firstly, the hot strip rolling is a complex nonlinear system, the looper model is linearized to be approximated around the operating point for simplicity. Secondly, the ISMC is addressed for LDs in hot strip rolling; meanwhile, due to the chattering phenomenon in conventional sliding mode control, a novel reaching law is designed; whereafter an off-policy RL algorithm is adopted to obtain the optimal controller. Finally, the effectiveness and superiority of the addressed method are elaborated by an example.