Neural-Network-Based Contouring Control for Robotic Manipulators in Operational Space

This brief presents a contouring control scheme for robotic manipulators. The geometric properties of the desired contour are incorporated in the controller design phase, and the resulting controller has been structured as a two-layered hierarchical control scheme that consists of an outer loop and an inner loop. The outer loop is formed by kinematic control system in operational space, which can be designed to assign different dynamics to the tangential, normal, and binormal direction of the desired contour. It is shown that the outer loop can provide a joint velocity reference signal to the inner one. The inner loop is used to implement a velocity servo control system at the robot joint level. Meanwhile, a radial basis function network is adopted to compensate for the nonlinear dynamics of the robotic manipulator, where a robust control strategy is used to suppress the modeling error of neural networks. Experimental results on the Zebra-Zero robotic manipulator have demonstrated the effectiveness of the proposed control scheme in comparison with other control strategies.

I. Introduction

Contouring tasks are important routines for robotic manipulators such as drawing a figure, welding, cutting, and deburring, etc. For these operations, contouring accuracy is one direct measure for evaluating the product quality. Generally, contouring accuracy can be represented by the contouring error defined to be the minimum distance from the position of the end-effector to the desired contour. Contouring control aimed at reducing the contouring error should attract great concerns especially for high-speed and high-precision applications.