Research Papers

Sliding Mode Control Based on Chemical Reaction Optimization and Radial Basis Functional Link Net for De-Icing Robot Manipulator

[+] Author and Article Information
Thuy Van Tran

College of Electrical and
Information Engineering,
Hunan University,
Changsha, Hunan 410082, China
Faculty of Electrical Engineering,
Hanoi University of Industry,
Hanoi 10000, Vietnam
e-mail: tranthuyvan.haui@gmail.com

YaoNan Wang

College of Electrical and
Information Engineering,
Hunan University,
Changsha, Hunan 410082, China
e-mail: yaonan@hnu.edu.cn

HungLinh Ao

Institute for Computational Science
and Faculty of Civil Engineering,
Ton Duc Thang University
Faculty of Mechanical Engineering,
Industrial University of Hochiminh City,
Hochiminh City 70000, Vietnam
e-mail: aohunglinh@tdt.edu.vn

Tung Khac Truong

Institute for Computational Science
and Faculty of Civil Engineering,
Ton Duc Thang University
Faculty of Information Technology,
Industrial University of Hochiminh City,
Hochiminh City 70000, Vietnam
e-mail: truongkhactung@tdt.edu.vn

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received April 17, 2014; final manuscript received October 20, 2014; published online December 10, 2014. Assoc. Editor: Jwu-Sheng Hu.

J. Dyn. Sys., Meas., Control 137(5), 051009 (May 01, 2015) (16 pages) Paper No: DS-14-1180; doi: 10.1115/1.4028886 History: Received April 17, 2014; Revised October 20, 2014; Online December 10, 2014

In this paper, a sliding mode control (SMC) system based on combining chemical reaction optimization (CRO) algorithm with radial basis functional link net (RBFLN) for an n-link robot manipulator is proposed to achieve the high-precision position tracking. In the proposed scheme, a three-layer RBFLN with powerful approximation ability is employed to approximate the uncertainties, such as parameter variations, friction forces, and external disturbances, and to eliminate chattering phenomenon of the SMC. In order to achieve the expected performance in the initial phase as well as the improved convergence rate, the RBFLN parameters need to be optimized in advance. Therefore, the initial parameters of the RBFLN are optimized offline by CRO algorithm instead of random selection. Furthermore, the RBFLN weights are determined online according to adaptive tuning laws in the sense of a projection algorithm and the Lyapunov stability theorem to guarantee the stability and convergence of the system. The simulation results of three-link de-icing robot manipulator (DIRM) are provided to verify the robustness and effectiveness of the proposed methodology.

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Fig. 1

Architecture of three-link DIRM

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Fig. 2

(a) Structure of RBFNN and (b) structure of RBFLN

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Fig. 3

Flow diagram of RBFLN parameters optimization based on CRO

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Fig. 4

Structure of CRLSMC system

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Fig. 7

Simulated responses of PD control system at link 1–3

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Fig. 12

Approximate error of ‖f(χ)‖ and ‖f∧(χ)‖ for RLSMC

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Fig. 5

(a) CTC system and (b) PD control system

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Fig. 6

Simulated responses of CTC system at link 1–3

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Fig. 9

Simulated responses of RLSMC system at link 1–3

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Fig. 10

Simulated responses of CRLSMC system at link 1–3

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Fig. 11

Approximate error of ‖f(χ)‖ and ‖f∧(χ)‖ for RNSMC

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Fig. 13

Approximate error of ‖f(χ)‖ and ‖f∧(χ)‖ for CRLSMC

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Fig. 8

Simulated responses of RNSMC system at link 1–3



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