Takagi-Sugeno-Kang fuzzy PID control for DC electrical machines

Luis Brito Palma; Rui Azevedo Antunes; Paulo Gil; Vasco Brito

doi:10.1109/CPE-POWERENG48600.2020.9161668

Takagi-Sugeno-Kang fuzzy PID control for DC electrical machines

Luis Brito Palma, Rui Azevedo Antunes, Paulo Gil, Vasco Brito

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

This paper deals with Takagi-Sugeno-Kang (TSK) type fuzzy PID controllers in the context of nonlinear dynamic systems. The design framework for tuning the controller gains depends on particle swarm optimization (PSO), assuming the nonlinear system approximated by an artificial neural network, leading to an overall robust control methodology based on TSK fuzzy PID control. Obtained data and information from simulations and experimental tests considering a nonlinear dynamic process including a DC electrical machine confirm the effectiveness of the control strategy.

Original language	English
Title of host publication	Proceedings - 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	309-316
Number of pages	8
ISBN (Electronic)	9781728142180
DOIs	https://doi.org/10.1109/CPE-POWERENG48600.2020.9161668
Publication status	Published - Jul 2020
Event	14th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020 - Virtual, Online, Portugal Duration: 8 Jul 2020 → 10 Jul 2020

Conference

Conference	14th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020
Country/Territory	Portugal
City	Virtual, Online
Period	8/07/20 → 10/07/20

Keywords

DC Electrical Machines
Nonlinear Neural Models
Nonlinear Systems
Particle Swarm Optimization
TSK Fuzzy PID Control

Access to Document

10.1109/CPE-POWERENG48600.2020.9161668

Cite this

Palma, L. B., Antunes, R. A., Gil, P., & Brito, V. (2020). Takagi-Sugeno-Kang fuzzy PID control for DC electrical machines. In Proceedings - 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020 (pp. 309-316). Article 9161668 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CPE-POWERENG48600.2020.9161668

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title = "Takagi-Sugeno-Kang fuzzy PID control for DC electrical machines",

abstract = "This paper deals with Takagi-Sugeno-Kang (TSK) type fuzzy PID controllers in the context of nonlinear dynamic systems. The design framework for tuning the controller gains depends on particle swarm optimization (PSO), assuming the nonlinear system approximated by an artificial neural network, leading to an overall robust control methodology based on TSK fuzzy PID control. Obtained data and information from simulations and experimental tests considering a nonlinear dynamic process including a DC electrical machine confirm the effectiveness of the control strategy. ",

keywords = "DC Electrical Machines, Nonlinear Neural Models, Nonlinear Systems, Particle Swarm Optimization, TSK Fuzzy PID Control",

author = "Palma, {Luis Brito} and Antunes, {Rui Azevedo} and Paulo Gil and Vasco Brito",

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doi = "10.1109/CPE-POWERENG48600.2020.9161668",

language = "English",

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Palma, LB , Antunes, RA , Gil, P & Brito, V 2020, Takagi-Sugeno-Kang fuzzy PID control for DC electrical machines. in Proceedings - 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020., 9161668, Institute of Electrical and Electronics Engineers (IEEE), pp. 309-316, 14th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020, Virtual, Online, Portugal, 8/07/20. https://doi.org/10.1109/CPE-POWERENG48600.2020.9161668

Takagi-Sugeno-Kang fuzzy PID control for DC electrical machines. / Palma, Luis Brito ; Antunes, Rui Azevedo ; Gil, Paulo et al.
Proceedings - 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020. Institute of Electrical and Electronics Engineers (IEEE), 2020. p. 309-316 9161668.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper deals with Takagi-Sugeno-Kang (TSK) type fuzzy PID controllers in the context of nonlinear dynamic systems. The design framework for tuning the controller gains depends on particle swarm optimization (PSO), assuming the nonlinear system approximated by an artificial neural network, leading to an overall robust control methodology based on TSK fuzzy PID control. Obtained data and information from simulations and experimental tests considering a nonlinear dynamic process including a DC electrical machine confirm the effectiveness of the control strategy.

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KW - Nonlinear Neural Models

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Takagi-Sugeno-Kang fuzzy PID control for DC electrical machines

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