Estimator and command filtering-based neural network control for flexible-joint robotic manipulators driven by electricity

Abstract

The article proposes an estimator and command filtering-based adaptive neural network controller for the electrically driven flexible-joint robotic manipulators with output constraints under the circumstance of matched and mismatched disturbances in system dynamics. The presented method is designed based on electrically driven model of the n-link flexible-joint robotic manipulators, which introduces more uncertainties and increases the dimensionality of the system but is more in line with practical. In view of the properties of fast convergence speed and great estimation performance in radial basis function neural network, radial basis function neural network is used to approximate the internal uncertain dynamic parameters of the system. An observer-based estimator is introduced for estimating the matched and mismatched disturbances in flexible-joint robotic manipulator dynamics. As to the differential explosion problem in backstepping control design, this article utilizes second-order command filters to overcome it. This article also adopts barrier Lyapunov functions for implementing output constraint to consider security issues in practical use. For demonstrating the effectiveness of the proposed controller, numerical simulations on two-link flexible-joint robotic manipulators are conducted. On the basis of the comparisons among estimator and command filtering-based adaptive neural network controller and other advanced controllers, the superiorities of estimator and command filtering-based adaptive neural network controller in several areas are proved.