Subspace-based identification for linear and nonlinear systems

• Published in: 2005 American Control Conference pp. 2320 - 2334 vol. 4
• Main Authors: Palanthandalam-Madapusi, H.J., Lacy, S., Hoagg, J.B., Bernstein, D.S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Analytical models; Control system synthesis; Mathematical model; Nonlinear dynamical systems; Nonlinear systems; Predictive models; State estimation; System analysis and design; System identification; Time domain analysis
• ISBN: 0780390989; 9780780390980; 9780780390997; 0780390997
• ISSN: 0743-1619
• DOI: 10.1109/ACC.2005.1470314

This paper deals with the basic subspace algorithm for time-invariant systems. A simplified proof of the fact that the state sequence and/or the observability matrix of the dynamical system can be determined directly from input-output data is provided. Some existing identification algorithms for linear time-varying systems are presented. The paper also covers the bulk of the existing subspace-based nonlinear identification algorithms including Hammerstein and nonlinear feedback identification, Hammerstein-Wiener identification for Wiener systems, linear parameter-varying system identification, and bilinear system identification.