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Adaptive Neural Composite Learning Finite-Time Control for a QUAV with Guaranteed Tracking Performance

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Cognitive Systems and Information Processing (ICCSIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1919))

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Abstract

This paper discusses the neural adaptive composite learning finite-time control for a quadrotor unmanned aerial vehicle (QUAV). Based on the command filter backstep** control (CFBC) scheme, the computational complexity caused by repetitive derivation and the inverse effect of the filter error are effectively removed. Moreover, the neural networks (NNs) are employed to identify the nonlinear coupling terms in the controlled vehicle, and the prediction error is introduced to adjust the composite learning law of neural weights, which improves the learning ability of NNs. Then, an adaptive neural composite learning finite-time prescribed performance controller is designed to achieve the convergence constraint on the tracking error, which makes the tracking error always in the planned steady-state region, and all variables of the closed-loop system are practical finite-time bounded. Finally, simulation results are shown to demonstrate the validity of the proposed method.

This work was supported in part by the National Natural Science Foundation of China under Grants 62203153, and in part by the Natural Science Fund for Young Scholars of Henan Province under Grant 222300420151.

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Authors and Affiliations

  1. School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China

    Chenglin Wu & Shuai Song

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  1. Chenglin Wu
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  2. Shuai Song
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Correspondence to Shuai Song .

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Editors and Affiliations

  1. Tsinghua University, Bei**g, China

    Fuchun Sun

  2. Southern University of Science and Technology, Shenzhen, China

    Qinghu Meng

  3. Henan University of Science and Technology, Luoyang, China

    Zhumu Fu

  4. Tsinghua University, Bei**g, China

    Bin Fang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Wu, C., Song, S. (2024). Adaptive Neural Composite Learning Finite-Time Control for a QUAV with Guaranteed Tracking Performance. In: Sun, F., Meng, Q., Fu, Z., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2023. Communications in Computer and Information Science, vol 1919. Springer, Singapore. https://doi.org/10.1007/978-981-99-8021-5_5

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  • DOI: https://doi.org/10.1007/978-981-99-8021-5_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8020-8

  • Online ISBN: 978-981-99-8021-5

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