LAPSE:2023.36412
Published Article

LAPSE:2023.36412
Design of Intelligent Nonlinear H2/H∞ Robust Control Strategy of Diesel Generator-Based CPSOGSA Optimization Algorithm
August 2, 2023
Abstract
In today’s human society, diesel generators (DGs) are widely applied in the human energy and electricity supply system due to its technical, operational, and economic advantages. This paper proposes an intelligent nonlinear H2/H∞ robust controller based on the chaos particle swarm gravity search optimization algorithm (CPSOGSA), which controls the speed and excitation of a DG. In this method, firstly, establish the nonlinear mathematical model of the DG, and then design the nonlinear H2/H∞ robust controller based on this. The direct feedback linearization and the H2/H∞ robust control theory are combined and applied. Based on the design of the integrated controller for DG speed and excitation, the system’s performance requirements are transformed into a standard robust H2/H∞ control problem. The parameters of the proposed solution controller are optimized by using the proposed CPSOGSA. The introduction of CPSOGSA completes the design of an intelligent nonlinear H2/H∞ robust controller for DG. The simulation is implemented in MATLAB/Simulink, and the results are compared with the PID control method. The obtained results prove that the proposed method can effectively improve the dynamic accuracy of the system and the ability to suppress disturbances and improve the stability of the system.
In today’s human society, diesel generators (DGs) are widely applied in the human energy and electricity supply system due to its technical, operational, and economic advantages. This paper proposes an intelligent nonlinear H2/H∞ robust controller based on the chaos particle swarm gravity search optimization algorithm (CPSOGSA), which controls the speed and excitation of a DG. In this method, firstly, establish the nonlinear mathematical model of the DG, and then design the nonlinear H2/H∞ robust controller based on this. The direct feedback linearization and the H2/H∞ robust control theory are combined and applied. Based on the design of the integrated controller for DG speed and excitation, the system’s performance requirements are transformed into a standard robust H2/H∞ control problem. The parameters of the proposed solution controller are optimized by using the proposed CPSOGSA. The introduction of CPSOGSA completes the design of an intelligent nonlinear H2/H∞ robust controller for DG. The simulation is implemented in MATLAB/Simulink, and the results are compared with the PID control method. The obtained results prove that the proposed method can effectively improve the dynamic accuracy of the system and the ability to suppress disturbances and improve the stability of the system.
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Keywords
diesel generator, intelligent optimization, robust control, speed and excitation control
Subject
Suggested Citation
Zou Y, Xiao B, Qian J, Xiao Z. Design of Intelligent Nonlinear H2/H∞ Robust Control Strategy of Diesel Generator-Based CPSOGSA Optimization Algorithm. (2023). LAPSE:2023.36412
Author Affiliations
Zou Y: School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
Xiao B: Faulty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650000, China
Qian J: Faulty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650000, China
Xiao Z: School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
Xiao B: Faulty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650000, China
Qian J: Faulty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650000, China
Xiao Z: School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
Journal Name
Processes
Volume
11
Issue
7
First Page
1867
Year
2023
Publication Date
2023-06-21
ISSN
2227-9717
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Original Submission
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PII: pr11071867, Publication Type: Journal Article
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LAPSE:2023.36412
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https://doi.org/10.3390/pr11071867
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[v1] (Original Submission)
Aug 2, 2023
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Calvin Tsay
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