Research on manipulator motion control based on neural network algorithms

The manipulator is the new artificial intelligence device, and its motion control is the basis for ensuring the stability of the manipulator's attitude. The traditional manipulator motion control adopts the static neuron control method, which will lead to small disturbance in the attitude control of the manipulator, and cause the stable motion performance of the manipulator. A motion control algorithm for manipulator is proposed based on variable structure fuzzy PID neural network. The coordinate system structure description and manipulator dynamics analysis of the controlled system are carried out. The motion control algorithm of the manipulator is improved by using variable structure PID neural network control and adaptive disturbance suppression method. Combined with the strict feedback control method, the motion error of the manipulator is compensated, and the steady-state error is corrected by the adaptive inertial compensation method to realise the motion control optimisation of the manipulator. The simulation results show that the motion control algorithm of the manipulator has better positioning performance and better control stability, reduces the steady-state error and improves the control stability.