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Learning control for discrete-time nonlinear systems with sensor saturation and measurement noises

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  • Dong Shen
  • Chao Zhang

Abstract

The iterative learning control (ILC) is investigated for a class of nonlinear systems with measurement noises where the output is subject to sensor saturation. An ILC algorithm is introduced based on the measured output information rather than the actual output signal. A decreasing sequence is also incorporated into the learning algorithm to ensure a stable convergence under stochastic noises. It is strictly proved with the help of the stochastic approximation technique that the input sequence converges to the desired input almost surely along the iteration axis. Illustrative simulations are exploited to verify the effectiveness of the proposed algorithm.

Suggested Citation

  • Dong Shen & Chao Zhang, 2017. "Learning control for discrete-time nonlinear systems with sensor saturation and measurement noises," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(13), pages 2764-2778, October.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:13:p:2764-2778
    DOI: 10.1080/00207721.2017.1344894
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    References listed on IDEAS

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    1. Jian Liu & Xiaoe Ruan, 2016. "Networked iterative learning control approach for nonlinear systems with random communication delay," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(16), pages 3960-3969, December.
    2. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
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