基于改进神经网络的空间机械臂阻抗控制方法

An impedance control method based on improved neural network was proposed oriented to compliance control of space manipulators under the condition of uncertain environmental information and unknown collision model. Based on the closed-loop equation of impedance control system, the reasons why precise force control can't be achieved under the condition of uncertain environmental information and unknown collision model were analyzed. The weight matrices in the neural network were adjusted by particle swarm optimization algorithm to improve the convergence speed and optimization performance of neural network. An impedance controller based on the improved neural network was proposed, which accomplished compliance control. The improved neural network can adjust the impedance parameters on line to achieve better compliance control effect. Numerical simulation results show that the proposed controller can reduce the force control error and position control error effectively, and has a better anti-jamming capability for force feedback interference signal than traditional impedance controller.