TY - GEN
T1 - Enhanced Grey Wolf Optimization for Hybrid Time–Frequency Identification of Fractional-Order Process Models
AU - Idir, Abdelhakim
AU - Gude, Juan J.
AU - Camacho, Oscar
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Fractional-order models provide a powerful framework for representing industrial processes exhibiting memory effects, anomalous diffusion, and distributed-parameter dynamics. This paper presents a hybrid time–frequency identification approach for fractional-order process models, optimized using an Enhanced Grey Wolf Optimizer (E-GWO) with adaptive leadership weighting. The proposed method integrates time-domain step response fitting and frequency-domain analysis into a unified cost function. Three fractional model structures are considered: FFOPDT, FSOPDT, and FHOPDT. Model performance is assessed in terms of parameter convergence, time-domain validation, frequency-domain accuracy, and estimation error. Simulation results indicate that the FHOPDT model outperforms lower-order alternatives on the selected benchmark, achieving significant improved fitness values and parameter estimation errors below 1%. The proposed framework offers a promising tool for the high-fidelity modeling of fractional-order industrial processes.
AB - Fractional-order models provide a powerful framework for representing industrial processes exhibiting memory effects, anomalous diffusion, and distributed-parameter dynamics. This paper presents a hybrid time–frequency identification approach for fractional-order process models, optimized using an Enhanced Grey Wolf Optimizer (E-GWO) with adaptive leadership weighting. The proposed method integrates time-domain step response fitting and frequency-domain analysis into a unified cost function. Three fractional model structures are considered: FFOPDT, FSOPDT, and FHOPDT. Model performance is assessed in terms of parameter convergence, time-domain validation, frequency-domain accuracy, and estimation error. Simulation results indicate that the FHOPDT model outperforms lower-order alternatives on the selected benchmark, achieving significant improved fitness values and parameter estimation errors below 1%. The proposed framework offers a promising tool for the high-fidelity modeling of fractional-order industrial processes.
KW - Enhanced Grey Wolf Optimization
KW - Fractional-order systems
KW - Hybrid cost function
KW - System identification
KW - Underdamped processes
UR - https://www.scopus.com/pages/publications/105042813097
U2 - 10.1007/978-3-032-29254-4_54
DO - 10.1007/978-3-032-29254-4_54
M3 - Contribución a la conferencia
AN - SCOPUS:105042813097
SN - 9783032292537
T3 - Communications in Computer and Information Science
SP - 675
EP - 686
BT - Soft Computing Models in Industrial and Environmental Applications - 21st International Conference, SOCO 2026, Proceedings
A2 - Corchado, Emilio
A2 - Quintián, Héctor
A2 - Jove, Esteban
A2 - Troncoso Lora, Alicia
A2 - Martínez Álvarez, Francisco
A2 - García Bringas, Pablo
A2 - Fosci, Paolo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2026
Y2 - 18 June 2026 through 19 June 2026
ER -