Tracking control based on neural network strategy for robot manipulator

Abstract

This study presents a sliding-mode neural-network (SMNN) control system for the tracking control of an n rigid-link robot manipulator to achieve high-precision position control. The aim of this study is to overcome some of the shortcomings of conventional robust controllers such as a model-based adaptive controller requires the system dynamics in detail; the fuzzy rule learning scheme has a latent stability problem; an adaptive control scheme for robot manipulator via fuzzy compensator requires strict constrained conditions and prior system knowledge. In the SMNN control system, a neural network controller is developed to mimic an equivalent control law in the sliding-mode control, and a robust controller is designed to curb the system dynamics on the sliding surface for guaranteeing the asymptotic stability property. Moreover, an adaptive bound estimation algorithm is employed to estimate the upper bound of uncertainties. All adaptive learning algorithms in the SMNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system whether the uncertainties occur or not. Computer simulations of a two-link robot manipulator verify the validity of the proposed control strategy in the possible presence of uncertainties and different trajectories. The proposed SMNN control scheme possesses two salient merits: (1) it guarantees the stability of the controlled system, and (2) no constrained conditions and prior knowledge of the controlled plant is required in the design process. This new intelligent methodology provides the designer with an alternative choice to control an n rigid-link robot manipulator.