The Research Progress on Adaptive PID Control Methods in Joint Space of Robotic Arms

The spatial coordination of robotic arm joints represents a significant obstacle in robotics. Despite its simplicity, traditional PID control is still extensively used owing to its straightforward implementation and dependable performance in many practical applications. However, traditional PID control has certain limitations when dealing with nonlinear, time-varying systems or complex environments. Thus, adaptive PID control effectively enhances the robustness and adaptability of the system by adjusting the gain parameters online. This paper reviews recent advances in adaptive PID control for robotic arm joint space, focusing on approaches such as online parameter tuning, model reference adaptive control (MRAC), and intelligent optimization techniques. The results show that online adjustment and MRAC methods have strong robustness in nonlinear and disturbed environments. The intelligent optimization method performs outstandingly in enhancing accuracy and response speed, but faces challenges in computational complexity and real-time performance. Moreover, the limitations of existing research are also discussed, and the future application prospects of combining deep learning and reinforcement learning with adaptive PID control are explored.