Hybrid Position/Force Controller Design of the Robot Manipulator Using Neural Networks

신경회로망을 이용한 로보트 매니률레이터의 하이브리드 위치/힘 제어기 설계

In this paper we propose a hybrid position/force controller of a robot manipulator using feedback error learning rule and neural networks. The neural network is constructed from inverse dynamics. The weighting value of each neuron is trained by using a feedback force as an error signal. If the neural networks are sufficiently trained well, it does not require the feedback-loop with error signals. The effectiveness of the proposed hybrid position/force controller is demonstrated by computer simulation using PUMA 560 manipulator.