Research on Dynamic Control of Robot Joint Based on Neural Network

Abstract

With the rapid development of intelligent manufacturing and automation technology, the dynamic control of robot joint has become the key technology to achieve high precision and high efficiency. Traditional control methods, such as PID control, although they perform well in some applications, their performance is limited in the face of complex nonlinear systems and real-time requirements. This paper reviews the robot joint dynamic control method based on neural network, and discusses the use of deep learning technology to improve the precision and adaptability of robot control. Firstly, the basic concepts of robot joint control are introduced, including position control, speed control and force/torque control. Then, the application of neural network in robot control is analyzed, including inverse dynamics modeling, trajectory tracking control and adaptive control. The paper further discusses various neural network structures and their applications in robot control. Finally, the paper identifies key challenges and future directions for neural network-based robot joint dynamic control. These include adaptive control strategies, integration of deep learning and reinforcement learning, and model predictive control (MPC). Other aspects like multi-modal perception and fusion, hardware implementation, edge computing, human-machine collaboration, interpretability, generalization, standardization, and modularization are also discussed. The purpose of this paper is to provide a comprehensive technical reference and guidance for future research directions for researchers and engineers in the field of robot control.