Construction and implementation of industrial robot motion control model based on deep learning

In this paper, a motion control model based on deep reinforcement learning (DRL) and lightweight design is proposed to meet the motion control requirements of industrial robots in complex scenes. This model combines the Soft Actor-Critic (SAC) algorithm and attention mechanism. Through state space decoupling and reward function optimization, the tracking accuracy of complex trajectory is significantly improved. In order to meet the real-time requirements of industrial scenes, the mixed precision quantization and knowledge distillation technology are used to compress the model parameters to 10% of the original model and realize millisecond reasoning. At the same time, the traditional controller constraint module is introduced, and Lyapunov stability analysis is combined to ensure the system security, and the transparency of model decision-making is enhanced by SHAP value. The experimental results show that the proposed model is superior to traditional control methods and other deep learning methods in trajectory tracking accuracy, real-time performance and safety, which provides a new solution for intelligent upgrading of industrial robots.