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Robust fault accommodation approach for double-pendulum tower cranes via adaptive neural network-triggered control

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Abstract

In this paper, a novel fault accommodation approach is designed for double-pendulum tower crane systems with both actuator drift and loss of efficiency. Importantly, a unique disturbance effect indicator is introduced to purposely judge the advantages and disadvantages of disturbances’ effects (including actuator faults, unknown/uncertain dynamics, unmodeled dynamics, and external disturbances) on the double-pendulum tower crane system. By employing the estimated disturbance, an adaptive neural network-triggered tracking strategy is subsequently developed. Additionally, utilizing the Lyapunov method and Barbalat’s lemma, the entire system stability is theoretically proven without any linearization around the equilibrium of original complicated nonlinear dynamics of tower cranes. The designed control strategy is not only able to deal with the double-pendulum swing dynamics, but also introduces a disturbance indicator for the first time to improve the tracking control performance by the positive disturbance effect. Several experimental results indicate that the designed strategy can achieve graceful degradation in tracking performance for the fault-tolerant system by employing the beneficial actuator faults, unknown/uncertain dynamics, unmodeled dynamics, and external disturbances while eliminating detrimental ones.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant Number 62273163); the General Research Fund of Hong Kong RGC (11202323); the Startup Fund of City University of Hong Kong (Grant Number 9380140); the Key R&D Project of Shandong Province (Grant Number 2022CXGC010503); the Youth Foundations of Shandong Province (Grant Number ZR202102230323); and the Project of Shandong Province Higher Educational Youth and Innovation Talent Introduction and Education Program.

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Authors and Affiliations

  1. School of Electrical Engineering, University of Jinan, Jinan, 250022, China

    Menghua Zhang & Weijie Huang

  2. Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China

    Xingjian Jing

  3. Shandong Zhengzhong Information Technology Co., LTD, Jinan, 250014, China

    Quanli Lu & Jiyang Chen

  4. Institute of Marine Science and Technology, Shandong University, Qingdao, 266237, China

    Jiyang Chen

  5. School of Control Science and Engineering, Shandong University, Jinan, 250000, China

    Zhenxue Chen

Authors
  1. Menghua Zhang
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  4. Quanli Lu
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Contributions

All authors contributed to the study conception and design. Material preparation, controller design, and stability analysis were performed by MZ and XJ. The first draft of the manuscript was written by MZ, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Xingjian Jing.

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Zhang, M., Jing, X., Huang, W. et al. Robust fault accommodation approach for double-pendulum tower cranes via adaptive neural network-triggered control. Nonlinear Dyn 111, 19993–20013 (2023). https://doi.org/10.1007/s11071-023-08891-z

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  • DOI: https://doi.org/10.1007/s11071-023-08891-z

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