Intelligent Adaptive Control Algorithm Based on Reinforcement Learning in the Field of Robotics

Although the traditional Proportional Integral Derivative (PID) control algorithm can solve the various uncertainties of the robot in the operation process, its parameter adjustment is highly difficult and does not well satisfy the demands of the system’s rapidity and accuracy. Therefore, this paper introduces an intelligent adaptive control algorithm (IACA) based on reinforcement learning by analyzing the research status of intelligent control algorithms in the field of robots. This algorithm can effectively solve the problems of traditional PID control algorithm, including strict model parameters, poor dynamic performance and long adjustment time. The article firstly introduces the basic principle of reinforcement learning, then elaborates the algorithm in detail and applies it to robot control, and finally conducts a simulation study. The experimental results indicate that the response time of the IACA based on reinforcement learning can reach 0.5 s at the shortest. It can be concluded that this method has a good effect in robot control, and the algorithm can effectively solve the problem that the traditional PID control algorithm requires strict model parameters.