Novel Neural Controllers for Kinematic Redundancy Resolution of Joint-Constrained Gough–Stewart Robot

Abstract

Parallel robots including Gough–Stewart platforms are widely applied in industrial factories and medical fields. This article investigates a kinematically redundant Gough–Stewart robot, designs, and compares three novel zeroing neural networks (ZNNs) to serve as the redundancy-resolution controllers. Unlike the existing ZNN controllers, the newly designed ZNN controllers are endowed with the capability to handle joint constraints by using a nonlinear complementarity problem function without introducing any extra hyperparameters, guaranteeing the safety of the robot. The proposed ZNN controllers are training-free, noniterative, and more accurate, as compared with other typical neural controllers for kinematic control of the Gough–Stewart robot. Theoretically, the convergence analyses of the ZNN controllers are rigorously carried out. Corresponding discrete neural controllers are established and then applied to the kinematically redundant Gough–Stewart robot with two path-tracking tasks exemplified. The path-tracking results comparatively substantiate the effectiveness and superiority of the ZNN controllers for redundancy resolution under joint constraints.