NDLI: Decentralized integral sliding mode control of reconfigurable manipulator: An enhancement methodology for practical application

Content Provider	IEEE Xplore Digital Library
Author	Zhao Bo Du Zhiyuan Li Yuanchun
Copyright Year	2015
Description	Author affiliation: Dept. of Control Eng., Changchun Univ. of Technol., Changchun, China (Zhao Bo; Li Yuanchun) \|\| Unit 92493, PLA, Huludao, China (Du Zhiyuan)
Abstract	A decentralized backstepping integral sliding mode control scheme is proposed for reconfigurable manipulators. Based on the Lyapunov stability theory, the backstepping technique is adopted to simplify the controller design process, and the integral sliding mode control is introduced due to its advantages such as robustness, simplicity, fast response. Furthermore, the Radial Basis Function Neural Networks (RBFNN), whose weights are updated by adaptive laws, are used to approximate or compensate the unknown term and interconnection term. In this scheme, it not only can follow the joint desired trajectories with high accuracy performance within a short time, but also reduce the serious chattering phenomenon harmful to the working life of the actuator. Moreover, the decentralized architecture could reflect the modularization property of reconfigurable manipulators sufficiently. In contrast to the controller with linear sliding mode and terminal sliding mode, the simulation results with two different configurations of reconfigurable manipulators show that the proposed scheme could achieve the satisfactory control performance, as well as easily be applied in practice.
Starting Page	3313
Ending Page	3318
File Size	366309
Page Count	6
File Format	PDF
ISBN	9789881563897
ISSN	19341768
DOI	10.1109/ChiCC.2015.7260150
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Technical Committee on Control Theory, Chinese Association of Automation
Subject Keyword	Backstepping RBF Neural Network Neural networks Decentralized control Integral sliding mode control Reconfigurable manipulator Trajectory Backstepping technique Joints Manipulator dynamics Sliding mode control
Content Type	Text
Resource Type	Article

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