%0 Journal Article
%T Adaptive Robust Quadratic Stabilization Tracking Control for Robotic System with Uncertainties and External Disturbances
%A Jinzhu Peng
%A Yan Liu
%J Journal of Control Science and Engineering
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/715250
%X An adaptive robust quadratic stabilization tracking controller with hybrid scheme is proposed for robotic system with uncertainties and external disturbances. The hybrid scheme combines computed torque controller (CTC) with an adaptive robust compensator, in which variable structure control (VSC) and optimal control approaches are adopted. The uncertain robot manipulator is mainly controlled by CTC, the VSC is used to eliminate the effect of the uncertainties and ensure global stability, and approach is designed to achieve a certain tracking performance of closed-loop system. A quadratic stability approach, which allows separate treatment of parametric uncertainties, is used to reduce the conservatism of the conventional robust control approach. It can be also guaranteed that all signals in closed-loop system are bounded. The validity of the proposed control scheme is shown by computer simulation of a two-link robotic manipulator. 1. Introduction Due to the uncertainties, disturbance, and nonlinear system dynamics, tracking control for robot manipulator always is a challenging problem [1, 2]. Therefore, in the past decades, many control approaches have been proposed and applied on controlling the robot manipulator, such as PID control [3], computed torque control method (CTC) [4¨C6], adaptive control [7, 8], variable structure control (VSC) [9, 10], robust control [11¨C16], fuzzy control [17¨C19], and neural networks control [20¨C24]. Varieties of hybrid control systems have been designed for controlling the complex robotic systems. Chang [25] and Wai [26] utilized neural networks to entirely approximate the equivalent control of VSC and then applied the technique to achieve a certain tracking performance. In these controllers, the robotic system nominal model is not included. Actually, the robotic nominal model could be known provided that the uncertainties are all considered to be reasonably modeled. For this reason, CTC could not be neglected in designing controller for complex robotic system due to its good performances [5], even though uncertainties exist in robotic system which would degrade the tracking performance. In order to eliminate the effect of the uncertainties, Song et al. [5] proposed an approach of CTC plus fuzzy compensator, the nominal system was controlled by using CTC method and for uncertain system, and a fuzzy controller acts as compensator. In [6], CTC plus a neural network compensator was proposed and simulations were conducted on a two-link robotic manipulator; furthermore, an experimental example was tested on PUMA560. However,
%U http://www.hindawi.com/journals/jcse/2014/715250/