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Mathematical Problems in Engineering
Volume 2015, Article ID 195720, 9 pages
http://dx.doi.org/10.1155/2015/195720
Research Article

Design Method of Active Disturbance Rejection Variable Structure Control System

1State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China
2School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
3Science and Technology on Aircraft Control Laboratory, Beijing 100191, China
4Beijing Aerospace Automatic Control Institute and National Key Laboratory of Science and Technology on Aerospace Intelligence Control, Beijing 100854, China

Received 6 July 2014; Revised 9 October 2014; Accepted 16 October 2014

Academic Editor: Xudong Zhao

Copyright © 2015 Yun-jie Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Based on lines cluster approaching theory and inspired by the traditional exponent reaching law method, a new control method, lines cluster approaching mode control (LCAMC) method, is designed to improve the parameter simplicity and structure optimization of the control system. The design guidelines and mathematical proofs are also given. To further improve the tracking performance and the inhibition of the white noise, connect the active disturbance rejection control (ADRC) method with the LCAMC method and create the extended state observer based lines cluster approaching mode control (ESO-LCAMC) method. Taking traditional servo control system as example, two control schemes are constructed and two kinds of comparison are carried out. Computer simulation results show that LCAMC method, having better tracking performance than the traditional sliding mode control (SMC) system, makes the servo system track command signal quickly and accurately in spite of the persistent equivalent disturbances and ESO-LCAMC method further reduces the tracking error and filters the white noise added on the system states. Simulation results verify the robust property and comprehensive performance of control schemes.