Composite learning sliding mode control of flexible-link manipulator

Bin Xu; Pengchao Zhang

doi:10.1155/2017/9430259

Composite learning sliding mode control of flexible-link manipulator

Bin Xu, Pengchao Zhang

Shaanxi University of Technology

Research output: Contribution to journal › Article › peer-review

44 Scopus citations

Abstract

This paper studies the control of a flexible-link manipulator with uncertainty. The fast and slow dynamics are derived based on the singular perturbation (SP) theory. The sliding mode control is proposed while the adaptive design is developed using neural networks (NNs) and disturbance observer (DOB) where the novel update laws for NN and DOB are designed. The closed-loop system stability is guaranteed via Lyapunov analysis. The effectiveness of the proposed method is verified via simulation test.

Original language	English
Article number	9430259
Journal	Complexity
Volume	2017
DOIs	https://doi.org/10.1155/2017/9430259
State	Published - 7 Aug 2017
Externally published	Yes

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title = "Composite learning sliding mode control of flexible-link manipulator",

abstract = "This paper studies the control of a flexible-link manipulator with uncertainty. The fast and slow dynamics are derived based on the singular perturbation (SP) theory. The sliding mode control is proposed while the adaptive design is developed using neural networks (NNs) and disturbance observer (DOB) where the novel update laws for NN and DOB are designed. The closed-loop system stability is guaranteed via Lyapunov analysis. The effectiveness of the proposed method is verified via simulation test.",

author = "Bin Xu and Pengchao Zhang",

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AB - This paper studies the control of a flexible-link manipulator with uncertainty. The fast and slow dynamics are derived based on the singular perturbation (SP) theory. The sliding mode control is proposed while the adaptive design is developed using neural networks (NNs) and disturbance observer (DOB) where the novel update laws for NN and DOB are designed. The closed-loop system stability is guaranteed via Lyapunov analysis. The effectiveness of the proposed method is verified via simulation test.

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Composite learning sliding mode control of flexible-link manipulator

Abstract

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