Fractional-Order System Identification: Efficient Reduced-Order Modeling with Particle Swarm Optimization and AI-Based Algorithms for Edge Computing Applications

Ignacio Fidalgo Astorquia (), Nerea Gómez-Larrakoetxea, Juan J. Gude and Iker Pastor
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Ignacio Fidalgo Astorquia: Department of Computing, Electronics and Communication Technologies, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain
Nerea Gómez-Larrakoetxea: DeustoTech-Deusto Institute of Technology, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain
Juan J. Gude: Department of Computing, Electronics and Communication Technologies, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain
Iker Pastor: Department of Computing, Electronics and Communication Technologies, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain

Mathematics, 2025, vol. 13, issue 8, 1-29

Abstract: Fractional-order systems capture complex dynamic behaviors more accurately than integer-order models, yet their real-time identification remains challenging, particularly in resource-constrained environments. This work proposes a hybrid framework that combines Particle Swarm Optimization (PSO) with various artificial intelligence (AI) techniques to estimate reduced-order models of fractional systems. First, PSO optimizes model parameters by minimizing the discrepancy between the high-order system response and the reduced model output. These optimized parameters then serve as training data for several AI-based algorithms—including neural networks, support vector regression (SVR), and extreme gradient boosting (XGBoost)—to evaluate their inference speed and accuracy. Experimental validation on a custom-built heating system demonstrates that both PSO and the AI techniques yield precise reduced-order models. While PSO achieves slightly lower error metrics, its iterative nature leads to higher and more variable computation times compared to the deterministic and rapid inference of AI approaches. These findings highlight a trade-off between estimation accuracy and computational efficiency, providing a robust solution for real-time fractional-order system identification on edge devices.

Keywords: fractional-order systems; reduced-order modeling; Particle Swarm Optimization; artificial intelligence; edge computing; real-time control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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