LAPSE:2023.12180v1
Published Article

LAPSE:2023.12180v1
Optimized Takagi−Sugeno Fuzzy Mixed H2/H∞ Robust Controller Design Based on CPSOGSA Optimization Algorithm for Hydraulic Turbine Governing System
February 28, 2023
Abstract
The hydraulic turbine governing system (HTGS) is a complex nonlinear system that regulates the rotational speed and power of a hydro-generator set. In this work, an incremental form of an HTGS nonlinear model was established and the Takagi−Sugeno (T-S) fuzzy linearization and mixed H2/H∞ robust control theory was applied to the design of an HTGS controller. A T-S fuzzy H2/H∞ controller for an HTGS based on modified hybrid particle swarm optimization and gravitational search algorithm integrated with chaotic maps (CPSOGSA) is proposed in this paper. The T-S fuzzy model of an HTGS that integrates multiple-state space equations was established by linearizing numerous equilibrium points. The linear matrix inequality (LMI) toolbox in MATLAB was used to solve the mixed H2/H∞ feedback coefficients using the CPSOGSA intelligent algorithm to optimize the weighting matrix in the process so that each mixed H2/H∞ feedback coefficients in the fuzzy control were optimized under the constraints to improve the performance of the controller. The simulation results show that this method allows the HTGS to perform well in suppressing system frequency deviations. In addition, the robustness of the method to system parameter variations is also verified.
The hydraulic turbine governing system (HTGS) is a complex nonlinear system that regulates the rotational speed and power of a hydro-generator set. In this work, an incremental form of an HTGS nonlinear model was established and the Takagi−Sugeno (T-S) fuzzy linearization and mixed H2/H∞ robust control theory was applied to the design of an HTGS controller. A T-S fuzzy H2/H∞ controller for an HTGS based on modified hybrid particle swarm optimization and gravitational search algorithm integrated with chaotic maps (CPSOGSA) is proposed in this paper. The T-S fuzzy model of an HTGS that integrates multiple-state space equations was established by linearizing numerous equilibrium points. The linear matrix inequality (LMI) toolbox in MATLAB was used to solve the mixed H2/H∞ feedback coefficients using the CPSOGSA intelligent algorithm to optimize the weighting matrix in the process so that each mixed H2/H∞ feedback coefficients in the fuzzy control were optimized under the constraints to improve the performance of the controller. The simulation results show that this method allows the HTGS to perform well in suppressing system frequency deviations. In addition, the robustness of the method to system parameter variations is also verified.
Record ID
Keywords
CPSOGSA, HTGS, mixed H2/H∞ controller, T-S fuzzy, wind power disturbances
Subject
Suggested Citation
Li L, Qian J, Zou Y, Tian D, Zeng Y, Cao F, Li X. Optimized Takagi−Sugeno Fuzzy Mixed H2/H∞ Robust Controller Design Based on CPSOGSA Optimization Algorithm for Hydraulic Turbine Governing System. (2023). LAPSE:2023.12180v1
Author Affiliations
Li L: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China [ORCID]
Qian J: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Zou Y: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Tian D: School of Global Public Health, New York University, New York, NY 10012, USA
Zeng Y: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China [ORCID]
Cao F: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Li X: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Qian J: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Zou Y: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Tian D: School of Global Public Health, New York University, New York, NY 10012, USA
Zeng Y: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China [ORCID]
Cao F: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Li X: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Journal Name
Energies
Volume
15
Issue
13
First Page
4771
Year
2022
Publication Date
2022-06-29
ISSN
1996-1073
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PII: en15134771, Publication Type: Journal Article
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LAPSE:2023.12180v1
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