Manipulator Tracking Algorithm Based on Estimated Dynamics and Time-Varying Output Constraint State

Abstract

In order to solve the buffeting problem of 7 DOF Manipulator caused by external disturbance in the motion process, the dynamic equation of the manipulator is given, the estimated inertia matrix is selected, and the RBF neural network fitting characteristics are used to fit the required items to reduce the difficulty of modeling. Based on the estimated dynamic model, a neural network adaptive control method with time-varying constraint state is proposed; The control law is designed, and the Lyapunov function equation and asymmetric term are established to derive its convergence. The angular displacement, angular velocity, angular acceleration, input torque and disturbance fitting are analyzed according to the joint state tracking results of the manipulator by using Simulink and gazebo simulation. The system simulation results show that the chattering phenomenon can be suppressed by using this method in the case of disturbance.