Design of intelligent optimal tracking control for robot manipulator

Abstract

An intelligent optimal control system is investigated for the tracking control of an n-link robot manipulator to achieve high-precision position control. In the intelligent optimal control system, a fuzzy neural network (FNN) controller is used to learn a nonlinear function in the optimal law, and a robust controller is designed to compensate the shortcoming of the FNN controller for further assuring the stable control performance. Moreover, an adaptive bound estimation algorithm is employed to estimate the upper bound of uncertainties. All adaptive learning algorithms in the intelligent optimal control technique and Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system whether the uncertainties occur or not. Numerical simulations of a three-link SCARA robot verify the validity of the proposed control strategy under the possible presence of uncertainty.