Decentralized control of robot joints based on neural network observer

In actual work, the system parameters of the robot joints will change in real time or cannot be measured, and the coupling relationship between the various subsystems and the existence of modeling errors make the system model difficult to determine. Based on such problems, a neural network observation is proposed Decentralized control method for robot joints of the robot. In the actual control of the manipulator, firstly, the model of each joint subsystem is established by the decentralized control theory, and the nonlinear function approximation ability of the neural network is used to approach the uncertain part of the manipulator subsystem online through input and output data. The design observer can estimate the state of the system, and use the estimated state to design a sliding mode controller to dynamically estimate and compensate the unknown model dynamics of each independent joint, and realize the self-control of the system when the speed information and model information are unknown. Adaptive control greatly enhances the robustness and adaptability of the robotic arm system. Finally, the stability criterion of the neural network observer and the sliding mode controller is given by the Lyapunov function method, and the simulation results prove the effectiveness of the design method.