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

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.