Neural network impedance force control of robot manipulator

Performance of impedance controller for robot force tracking is aaected by the uncertainties in both the robot dynamic model and environment stiiness. The purpose of this paper is to improve the controller ro-bustness by applying the neural network(NN) technique to compensate for the uncertainties in the robot model. NN control techniques are applied to two impedance control methods : torque-based and position-based impedance control, which are distinguished by the way of the impedance functions being implemented. A novel error signal is proposed for the neural network training. In addition, a trajectory modiication algorithm is developed to determine the reference trajectory when the environment stiiness is unknown. The robustness analysis of this algorithm to force sensor noise and inaccurate environment position measurement is also presented. The performances of the two NN impedance control schemes are compared by computer simulations. Simulation results based on a three degrees-of-freedom robot show that highly robust position/force tracking can be achieved under the presence of large uncertainties and force sensor noise.