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Composite Learning Sliding Mode Control of Flexible-Link Manipulator

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  • Bin Xu
  • Pengchao Zhang

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.

Suggested Citation

  • Bin Xu & Pengchao Zhang, 2017. "Composite Learning Sliding Mode Control of Flexible-Link Manipulator," Complexity, Hindawi, vol. 2017, pages 1-6, August.
    • Handle: RePEc:hin:complx:9430259
    DOI: 10.1155/2017/9430259
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    References listed on IDEAS

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    1. Bin Xu & Pengchao Zhang, 2017. "Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope," Complexity, Hindawi, vol. 2017, pages 1-8, July.
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    Cited by:

    1. Jin-yong Ju & Yufei Liu & Honglin Kan & Chunrui Zhang, 2019. "Master-Slave Composite Vibration Control of a Mobile Flexible Manipulator via Synchronization Optimization of Observation and Feedback," Complexity, Hindawi, vol. 2019, pages 1-13, December.
    2. Zhijia Zhao & Yonghao Ma & Guiyun Liu & Dachang Zhu & Guilin Wen, 2019. "Vibration Control of an Axially Moving System with Restricted Input," Complexity, Hindawi, vol. 2019, pages 1-10, January.
    3. Min Wang & Huiping Ye & Zhiguang Chen, 2017. "Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot," Complexity, Hindawi, vol. 2017, pages 1-14, October.

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