Nature–Inspired Metaheuristic Optimization for Control Tuning of Complex Systems
In this contribution, a methodology for the optimal tuning of controllers of complex systems based on meta–heuristic techniques is proposed. Two bio-inspired meta-heuristic optimization algorithms –the Antlion Optimizer (ALO) and the Whale Optimization Algorithm (WOA)– have been applied to two diffe...
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MDPI AG
2024-12-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/1/13 |
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| author | Jesús Garicano-Mena Matilde Santos |
| author_facet | Jesús Garicano-Mena Matilde Santos |
| author_sort | Jesús Garicano-Mena |
| collection | DOAJ |
| description | In this contribution, a methodology for the optimal tuning of controllers of complex systems based on meta–heuristic techniques is proposed. Two bio-inspired meta-heuristic optimization algorithms –the Antlion Optimizer (ALO) and the Whale Optimization Algorithm (WOA)– have been applied to two different dynamic systems: the Hoop & Ball electromechanical system, a system where a linearized description is adequate; and to a Wind Turbine–Generator–Rectifier, as an example of a complex non-linear dynamic system. The performance of the ALO and WOA techniques for the tuning of conventional PID controllers is evaluated in relation to the number of agents <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>n</mi><mi>S</mi></msub></semantics></math></inline-formula> and the maximum number of iterations <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>n</mi><mrow><mi>M</mi><mi>a</mi><mi>x</mi><mi>I</mi><mi>t</mi><mi>e</mi><mi>r</mi></mrow></msub></semantics></math></inline-formula>; given the stochastic nature of both methods, repeatability is also addressed. Finally, the computational effort required for their implementation is considered. By analyzing the obtained metrics, it is observed that both methods provide comparable results for the two systems considered and, therefore, the ALO and WOA techniques can complement each other by exploiting the advantages of each of them in controller tuning. |
| format | Article |
| id | doaj-art-79f390fb5ace464f82ac9a977a89b254 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-79f390fb5ace464f82ac9a977a89b2542025-01-24T13:24:35ZengMDPI AGBiomimetics2313-76732024-12-011011310.3390/biomimetics10010013Nature–Inspired Metaheuristic Optimization for Control Tuning of Complex SystemsJesús Garicano-Mena0Matilde Santos1ETSI Aeronáutica y del Espacio—Universidad Politécnica de Madrid, 28040 Madrid, SpainInstitute of Knowledge Technology, Complutense University of Madrid, 28040 Madrid, SpainIn this contribution, a methodology for the optimal tuning of controllers of complex systems based on meta–heuristic techniques is proposed. Two bio-inspired meta-heuristic optimization algorithms –the Antlion Optimizer (ALO) and the Whale Optimization Algorithm (WOA)– have been applied to two different dynamic systems: the Hoop & Ball electromechanical system, a system where a linearized description is adequate; and to a Wind Turbine–Generator–Rectifier, as an example of a complex non-linear dynamic system. The performance of the ALO and WOA techniques for the tuning of conventional PID controllers is evaluated in relation to the number of agents <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>n</mi><mi>S</mi></msub></semantics></math></inline-formula> and the maximum number of iterations <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>n</mi><mrow><mi>M</mi><mi>a</mi><mi>x</mi><mi>I</mi><mi>t</mi><mi>e</mi><mi>r</mi></mrow></msub></semantics></math></inline-formula>; given the stochastic nature of both methods, repeatability is also addressed. Finally, the computational effort required for their implementation is considered. By analyzing the obtained metrics, it is observed that both methods provide comparable results for the two systems considered and, therefore, the ALO and WOA techniques can complement each other by exploiting the advantages of each of them in controller tuning.https://www.mdpi.com/2313-7673/10/1/13Metaheuristics AlgorithmWhale Optimization Algorithm (<tt>WOA</tt>)Antlion Optimization Algorithm (<tt>ALO</tt>)complex dynamics systemsHoop & Ball electromechanical systemwind energy conversion system |
| spellingShingle | Jesús Garicano-Mena Matilde Santos Nature–Inspired Metaheuristic Optimization for Control Tuning of Complex Systems Biomimetics Metaheuristics Algorithm Whale Optimization Algorithm (<tt>WOA</tt>) Antlion Optimization Algorithm (<tt>ALO</tt>) complex dynamics systems Hoop & Ball electromechanical system wind energy conversion system |
| title | Nature–Inspired Metaheuristic Optimization for Control Tuning of Complex Systems |
| title_full | Nature–Inspired Metaheuristic Optimization for Control Tuning of Complex Systems |
| title_fullStr | Nature–Inspired Metaheuristic Optimization for Control Tuning of Complex Systems |
| title_full_unstemmed | Nature–Inspired Metaheuristic Optimization for Control Tuning of Complex Systems |
| title_short | Nature–Inspired Metaheuristic Optimization for Control Tuning of Complex Systems |
| title_sort | nature inspired metaheuristic optimization for control tuning of complex systems |
| topic | Metaheuristics Algorithm Whale Optimization Algorithm (<tt>WOA</tt>) Antlion Optimization Algorithm (<tt>ALO</tt>) complex dynamics systems Hoop & Ball electromechanical system wind energy conversion system |
| url | https://www.mdpi.com/2313-7673/10/1/13 |
| work_keys_str_mv | AT jesusgaricanomena natureinspiredmetaheuristicoptimizationforcontroltuningofcomplexsystems AT matildesantos natureinspiredmetaheuristicoptimizationforcontroltuningofcomplexsystems |