Masked Multiple State Space Model Identification Using FRD and Evolutionary Optimization

• Published in: IEEE transactions on industrial informatics 2024-07, Vol.20 (7), p.9861-9869
• Main Authors: Efe, Mehmet Onder, Kurkcu, Burak, Kasnakoglu, Cosku, Mohamed, Zaharuddin, Liu, Zhijie
• Format: Article
• Language: English
• Subjects: Algorithms; Costs; Dynamic models; Dynamical systems; Frequency response; Genetic algorithms; Genetic algorithms (GAs); identification; masked models; Optimization; Parameters; Social factors; State space models; State-space methods; System effectiveness; Time-frequency analysis
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2024.3388605

Identification of dynamical systems from frequency response data (FRD) has extensively been studied and effective techniques have been developed. Given different FRD sets obtained from different systems and a fixed state space model structure, is it possible to find a constant parameter vector containing (\mathbf {A},\mathbf {B},\mathbf {C},\mathbf {D}) quadruple's numerical content and a FRD-associated mask vector set that approximates the spectral information available in each FRD set? This article proposes a genetic algorithm based optimization approach to determine the real parameter vector (\mathbf {A},\mathbf {B},\mathbf {C},\mathbf {D}) and the binary mask vector through a sequential optimization scheme. We study state space models for matching FRD from multiple systems. Results show that the proposed optimization approach solves the problem and compresses multiple dynamical models into a single masked one.