An Iterative Reinforcement Learning Algorithm for Speed Drop Compensation in Rolling Mills

In the process of steel rolling production, the speed reduction compensation of the rolling mill is a key link to ensure the stability of slab rolling and product quality. This paper proposes a hybrid compensation method that integrates motor dynamic modeling with reinforcement learning to minimize mass flow error between adjacent rolling mills during slab rolling. A two-stage compensation strategy is designed, consisting of a constant-gain compensation phase followed by a decaying compensation phase, which explicitly accounts for the repetitive and consistent rolling conditions in batch slab production. Based on a motor dynamics-based theoretical model, an initial estimation of compensation parameters is first obtained, providing a physically interpretable starting point for optimization. Subsequently, a Deep Deterministic Policy Gradient (DDPG) algorithm is employed to iteratively refine the compensation parameters by learning from the mass flow error of each rolled slab, enabling data-driven adaptation while preserving physical consistency. Simulation results demonstrate that the proposed hybrid approach significantly reduces the mass flow error and achieves stable convergence, outperforming strategies with randomly initialized parameters. The results verify the effectiveness and novelty of the proposed method in combining model-based insight with reinforcement learning for intelligent and adaptive rolling mill speed drop compensation.