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Online Tuning of PID Controllers Based on Membrane Neural Computing
Mälardalen University.
Mälardalen University.
Mälardalen University.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
2023 (English)In: Lecture Notes on Data Engineering and Communications Technologies, Springer Science and Business Media Deutschland GmbH , 2023, Vol. 153, p. 455-464Chapter in book (Other academic)
Abstract [en]

PID controllers are still popular in a wide range of engineering practices due to their simplicity and robustness. Traditional design of a PID controller needs manual setting of its parameters in advance. This paper proposes a new method for online tuning of PID controllers based on hybridized neural membrane computing. A neural network is employed to adaptively determine the proper values of the PID parameters in terms of evolving situations/stages in the control process. Further the learning of the neural network is performed based on a membrane algorithm, which is used to locate the weights of the network to optimize the control performance. The effectiveness of the proposed method has been demonstrated by the preliminary results from simulation tests.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2023. Vol. 153, p. 455-464
Keywords [en]
Bioinformatics, Controllers, Electric control equipment, Membranes, Proportional control systems, Tuning, Engineering practices, Gain tuning, Membrane algorithm, Membrane computing, Neural computing, Neural membranes, Neural-networks, Online gain tuning, Online tuning, PID controllers, Three term control systems, Neural network, PID controller
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-61961DOI: 10.1007/978-3-031-20738-9_52ISI: 000964184200052Scopus ID: 2-s2.0-85147852132OAI: oai:DiVA.org:mdh-61961DiVA, id: diva2:1738687
Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2024-01-18Bibliographically approved

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Xiong, Ning

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CiteExportLink to record
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  • apa
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Language
  • de-DE
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  • nn-NO
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