Performance tuning for robot manipulators using intelligent robust controller

Abstract

This paper introduces a robust controller with performance tuning for robot manipulators. A new hybrid idea based on feedback linearization, Lyapunov’s second method and the genetic algorithm (GA) are applied for designing an intelligent robust controller. The inverse dynamic method is used to design a controller linearizing and decoupling the nonlinear manipulator dynamics. Because of the uncertainties in manipulator dynamics, the additional controller is used to achieve robustness designed by Lyapunov’s second method. Hence, robust stability is satisfied for manipulator dynamics. To attain robust performance, an uncertainty area is defined in an error phase portrait which must be minimized. In order to optimize this criterion, GA is employed to find the robust controller parameters. In fact, our criterion in designing the intelligent robust controller is both robust stability and robust performance simultaneously. Simulation and experimental results verify the proposed method through a four-bar linkage robot as a case study. The introduced theory might be considered as a promising way for the wide range class of nonlinear dynamic systems with uncertainties. Experiments are implemented on the robot using xPC target bearing the Hardware In the Loop property.