Systems Engineering and Electronics ›› 2020, Vol. 42 ›› Issue (3): 660-666.doi: 10.3969/j.issn.1001-506X.2020.03.021

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Open-loop PD-type iterative learning control with adaptive nonlinear gain

Mingguang DAI(), Rong QI(), Bingqiang LI(), Yiyun ZHAO()   

  1. School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
  • Received:2019-07-11 Online:2020-03-01 Published:2020-02-28
  • Supported by:
    国家自然科学基金(51777170);陕西省重点研究开发计划(2018ZDCXL-GY-05-07-01)

Abstract:

An open-loop PD-type iterative learning nonlinear gain adaptive algorithm based on the error amplitude and error rate of change is proposed for a class of nonlinear systems with both periodic disturbance and random measurement noise. The adjustment rules of the proportional and differential nonlinear gain are given respectively, and the proposed algorithm is analyzed theoretically and the convergence conditions are given. The results show that compared with the traditional open-loop PD-type iterative learning law with the fixed learning gain, when the nonlinear system has both periodic disturbance and measurement noise with large amplitude, the adaptive learning law of the nonlinear gain can regulate the proportional and differential learning gain online according to the error amplitude and error change rate, and suppress the disturbance and noise. Using this algorithm, the convergence speed of iterative learning is guaranteed under the high gain in the initial stage of learning, and the convergence precision and robustness are stronger under the small gain in the final stage of learning, and the error tracking curve obtained is smoother.

Key words: iterative learning control, open-loop PD learning law, nonlinear gain, periodic disturbance, random measurement noise

CLC Number: 

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