A Disturbance Compensation Method Using Adaptive Neural Network for Robotic Manipulator

Abstract

In contact with the hardest human tissues, such as bones and teeth, performing force control precisely is the key for improving operation effect and ensuring surgical safety. This article introduces an intuitive six degree-of-freedoms (6-DoF) task space PD control to track a given trajectory based on a linear and decoupled model for six dimensional pose (rotational and positional) displacement. Then, an adaptive neural network (NN) controller is designed to deal with the nonlinearities of the system. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the manipulator’s dynamic uncertainties. The stability of the controller is proved by using Lyapunov stability principles. Finally, the effectiveness of the proposed methods are validated through a group of setpoint control and trajectory tracking control simulations on a redundant robotic manipulator. The mean error of setpoint control with robotic uncertainties after compensation was 4. 47×1$0^{-9}$m, 1.42×1$0^{-8}$m, -1.22×1$0^{-7}$m, in axis X, Y, Z respectively. The maximum error of trajectory tracking in task space was less than 1 mm.