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Adaptive Radial-Basis Function Neural Network Control of a Pneumatic Actuator

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Intelligent Systems and Networks (ICISN 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 752))

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Abstract

This paper explores an advanced adaptive controller for Pneumatic Artificial Muscles (PAMs). PAMs offer lightweight, simple, and safe operation advantages but are difficult to model and control due to the non-linearity and hysteresis caused by their physical manufacturing. An adaptive controller based on a neural approximation is proposed to address these challenges, incorporating Radial Basis Function algorithms and adapting to model parameter uncertainty. The efficiency of the control approach is confirmed through a multi-scenario experiment with high-accuracy results, promising future developments in intelligent control of PAMs.

This research is funded by Hanoi University of Science and Technology (HUST) under project number T2022 - PC - 002.

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Authors and Affiliations

  1. Hanoi University of Science and Technology, Hanoi, Vietnam

    Van-Vuong Dinh, Bao-Long Pham, Viet-Thanh Nguyen, Minh-Duc Duong & Quy-Thinh Dao

  2. Hanoi Vocational College of High Technology, Hanoi, Vietnam

    Van-Vuong Dinh

Authors
  1. Van-Vuong Dinh
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  2. Bao-Long Pham
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  3. Viet-Thanh Nguyen
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  4. Minh-Duc Duong
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  5. Quy-Thinh Dao
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Corresponding author

Correspondence to Quy-Thinh Dao .

Editor information

Editors and Affiliations

  1. A1 Building, Hanoi University of Industry, Bac Tu Liem, Hanoi, Vietnam

    Thi Dieu Linh Nguyen

  2. School of Engineering and Technology, Universidad Internacional De La Rioja, Logroño (La Rioja), Spain

    Elena Verdú

  3. Swinburne University of Technology, Hanoi, Vietnam

    Anh Ngoc Le

  4. Warsaw University of Technology, Warsaw, Poland

    Maria Ganzha

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Dinh, VV., Pham, BL., Nguyen, VT., Duong, MD., Dao, QT. (2023). Adaptive Radial-Basis Function Neural Network Control of a Pneumatic Actuator. In: Nguyen, T.D.L., Verdú, E., Le, A.N., Ganzha, M. (eds) Intelligent Systems and Networks. ICISN 2023. Lecture Notes in Networks and Systems, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-99-4725-6_32

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  • DOI: https://doi.org/10.1007/978-981-99-4725-6_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4724-9

  • Online ISBN: 978-981-99-4725-6

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