Control of Networked Robotic Manipulator via ILC and Minimum Entropy

Author

Zhang, Jianhua ; Wang, Hong

Author_Institution

Dept. of Autom., North China Electr. Power Univ., Beijing

Volume

2

fYear

0

fDate

0-0 0

Firstpage

9499

Lastpage

9503

Abstract

This paper presents a novel feedback control method for networked control systems by combining the P-type iterative learning control (ILC) idea with minimum tracking error entropy control strategy. In specific, the time-delayed control system of robotic manipulator is considered, where the convergence of the proposed control law is proved. The stochastic characteristics of the tracking error are analyzed. Moreover, the procedure to solve the optimal iterative operator is given by using particle swarm optimization (PSO) techniques. Simulation results are provided to show the effectiveness of the proposed approach

Keywords

adaptive control; delays; feedback; iterative methods; learning systems; manipulators; minimum entropy methods; particle swarm optimisation; P-type iterative learning control; feedback control; minimum entropy; minimum tracking error entropy control; networked control systems; networked robotic manipulator; optimal iterative operator; particle swarm optimization; time-delay systems; Control systems; Convergence; Entropy; Error correction; Feedback control; Iterative methods; Manipulators; Networked control systems; Robot control; Stochastic processes; entropy; iterative learning control; optimization; robotic manipulator; time-delay systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713842

Filename

1713842