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Adaptive Neural Network Control of Serial Variable Stiffness Actuators

Author

Listed:
  • Zhao Guo
  • Yongping Pan
  • Tairen Sun
  • Yubing Zhang
  • Xiaohui Xiao

Abstract

This paper focuses on modeling and control of a class of serial variable stiffness actuators (SVSAs) based on level mechanisms for robotic applications. A multi-input multi-output complex nonlinear dynamic model is derived to fully describe SVSAs and the relative degree of the model is determined accordingly. Due to nonlinearity, high coupling, and parametric uncertainty of SVSAs, a neural network-based adaptive control strategy based on feedback linearization is proposed to handle system uncertainties. The feasibility of the proposed approach for position and stiffness tracking of SVSAs is verified by simulation results.

Suggested Citation

  • Zhao Guo & Yongping Pan & Tairen Sun & Yubing Zhang & Xiaohui Xiao, 2017. "Adaptive Neural Network Control of Serial Variable Stiffness Actuators," Complexity, Hindawi, vol. 2017, pages 1-9, November.
  • Handle: RePEc:hin:complx:5361246
    DOI: 10.1155/2017/5361246
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    References listed on IDEAS

    as
    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.
    2. Rong Mei & ChengJiang Yu, 2017. "Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation," Complexity, Hindawi, vol. 2017, pages 1-12, August.
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