To enhance controller performance, the optimization of control parameters has emerged as a critical research area. Among the array of optimization algorithms, the modified elite opposition-based artificial hummingbird algorithm (m-AHA) stands out for its ability to emulate behavioral strategies of hummingbirds and elite opposition-based technique. This paper, therefore, proposes m-AHA optimizer as a novel approach to optimize control parameters in a three-tanks liquid level system. By fine-tuning the parameters of proportional-integral-derivative (PID) controller, superior performance is achieved. Comparative evaluations with competitive algorithms, including the arithmetic optimization algorithm with Harris hawks optimization and covariance matrix adaptation evolution strategy, assess the m-AHA optimizer-based approach for three-tank liquid level system control. The ITAE (integral of time multiplied absolute error) performance index analyzes time domain and frequency metrics, revealing the outstanding performance of the m-AHA optimizer-based approach.
To enhance controller performance, the optimization of control parameters has emerged as a critical research area. Among the array of optimization algorithms, the modified elite opposition-based artificial hummingbird algorithm (m-AHA) stands out for its ability to emulate behavioral strategies of hummingbirds and elite opposition-based technique. This paper, therefore, proposes m-AHA optimizer as a novel approach to optimize control parameters in a three-tanks liquid level system. By fine-tuning the parameters of proportional-integral-derivative (PID) controller, superior performance is achieved. Comparative evaluations with competitive algorithms, including the arithmetic optimization algorithm with Harris hawks optimization and covariance matrix adaptation evolution strategy, assess the m-AHA optimizer-based approach for three-tank liquid level system control. The ITAE (integral of time multiplied absolute error) performance index analyzes time domain and frequency metrics, revealing the outstanding performance of the m-AHA optimizer-based approach.
Primary Language | English |
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Subjects | Evolutionary Computation |
Journal Section | PAPERS |
Authors | |
Publication Date | October 18, 2023 |
Submission Date | August 19, 2023 |
Acceptance Date | August 26, 2023 |
Published in Issue | Year 2023 |
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