NN controller of the constrained robot under unknown constraint

摘要

In this paper, the problems faced in the constrained force control is studied (uncertainties in dynamic model and the unknown constraints). A neural network (NN) controller is proposed based on the derived dynamic model of robot in the task space. The feed-forward neural network is used to adaptively compensate for the uncertainties in the robot dynamics. Training signals are proposed for the feed-forward neural network controller. The NN weights are tuned online, with no off-line learning phase required. An online estimation algorithm is developed to estimate the local shape of the constraint surface by using measured data on the force and position of the end-effector. The suggested controller is simple in structure and can be implemented easily. Real-time experiments are conducted using the five-bar robot to demonstrate the effectiveness of the proposed controller.