Pole assignment self-tuning controller for robotic manipulators

Abstract

This paper presents an effective adaptive controller for robotic manipulators. A perturbation difference model of the manipulator is established for the first time, based on which a modified pole assignment self-tuning control algorithm is developed. The controller is designed such that the variance of a generalized cost function is minimized and the controller parameters are estimated directly. Closed-loop pole assignment is achieved by adjusting on-line the weighting factors in the cost function. Simulations to a manipulator with three degrees of freedom are given to demonstrate the effectiveness of this self-tuning controller.