Control Method of Parallel Robot Based on Adaptive Neural Fuzzy Inference Combined with PID Control

The parallel robot has the advantages of simple structure, high rigidity, high positioning accuracy and fast dynamic response, especially suitable for occasions with high precision, large load and small working space. Due to the uncertainty, high nonlinearity and strong coupling of the parallel robot model, the traditional control method is difficult to obtain the ideal control effect. A neural network adaptive control algorithm based on particle swarm optimization is proposed in this paper. Algorithm combined with the traditional PID control, BP neural network and PSO global optimization algorithm, uses PSO to optimize the initial weights of BP neural network, and then uses the optimized adaptive neural network to adjust the PID parameters online.