Research on reinforcement learning for adaptive control of sand and gravel crushing processes

Artificial intelligence algorithms demonstrate exceptional potential for decision optimization in adaptive control of complex industrial processes. Addressing nonlinear coupling, signal fluctuations, and operational uncertainties in sand and gravel crushing processes, this study employs reinforcement learning to construct a multimodal perception-driven adaptive control framework. The objective is to achieve dynamic adjustment of crushing parameters and energy consumption optimization through intelligent algorithms, thereby enhancing system stability and precision. Methodologically, Deep Reinforcement Learning (DRL) integrates signal and image features to construct the state space. Based on the DQN algorithm, state perception, reward functions, and policy network structures are designed. Adaptive learning rates and online update mechanisms are introduced to enable real-time iteration of control strategies. Results demonstrate that during experimental platform validation, this model significantly reduces crushing particle size error by 57.3%, lowers energy consumption by 29.3%, and shortens response time by 46.4% compared to traditional PID and fuzzy control methods, achieving rapid convergence and stable control under complex operating conditions. Conclusions indicate that the reinforcement learning-based adaptive control architecture effectively enhances the intelligence level of sand and gravel crushing systems, providing an extensible and transferable algorithmic pathway for industrial process control with broad engineering application prospects.