Control of Intelligent Mechatronic Systems Incorporating Reinforcement Learning Algorithms

This study develops a reinforcement learning-based control method for intelligent mechatronic systems, integrating system modeling, algorithm design, and experimental validation. By combining deep reinforcement learning with physical modeling, it enhances decision efficiency and control accuracy in high-dimensional spaces. Experimental results show superior trajectory tracking, anti-interference, and real-time performance over traditional methods, providing theoretical and technical support for efficient intelligent control.