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International Journal of Automation and Computing 2018, Vol. 15 Issue (2) :239-248    DOI: 10.1007/s11633-018-1122-2
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Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks
Hong-Jun Yang, Min Tan
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Abstract This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function (RBF) neural network. The flexible manipulator in this paper is considered to be an Euler-Bernoulli beam. We first obtain a partial differential equation (PDE) model of single-link flexible manipulator by using Hamiltons approach. To improve the control robustness, the system uncertainties including modeling uncertainties and external disturbances are compensated by an adaptive neural approximator. Then, a sliding mode control method is designed to drive the joint to a desired position and rapidly suppress vibration on the beam. The stability of the closed-loop system is validated by using Lyapunov s method based on infinite dimensional model, avoiding problems such as control spillovers caused by traditional finite dimensional truncated models. This novel controller only requires measuring the boundary information, which facilitates implementation in engineering practice. Favorable performance of the closed-loop system is demonstrated by numerical simulations.
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KeywordsSliding mode control   adaptive control   neural network   flexible manipulator   partial differential equation (PDE)     
Received: 2018-01-21; Revised: 2018-02-23; published: 2018-01-21
Fund:

This work was supported by National Natural Science Foundation of China (No. 61703402).

Corresponding Authors: Min Tan     Email: min.tan@ia.ac.cn
About author: Hong-Jun Yang received the Ph. D. degree in School of Automation Science and Electrical Engineering, Beihang University, China in 2016. E-mail:dzyangyang@126.com;Min Tan received the B. Sc. degree in control engineering from Tsinghua University, China in 1986, and the Ph. D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, China in 1990.E-mail:min.tan@ia.ac.cn
Cite this article:   
Hong-Jun Yang, Min Tan. Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks[J]. International Journal of Automation and Computing , vol. 15, no. 2, pp. 239-248, 2018.
URL:  
http://www.ijac.net/EN/10.1007/s11633-018-1122-2      或     http://www.ijac.net/EN/Y2018/V15/I2/239
 
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