Identification of systems with localised nonlinearity: from state-space to block-structured models

DOI10.1016/j.automatica.2013.01.052zbMath1319.93020OpenAlexW2038334902MaRDI QIDQ490609

Laurent Vanbeylen, Anne Van Mulders, Johan Schoukens

Publication date: 27 August 2015

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2013.01.052

zbMATH Keywords

parameter estimationnonlinear systemsnonlinear modelsstate-space modelsidentification algorithmsblock-structured models

Mathematics Subject Classification ID

System identification (93B30)

Related Items (10)

Identification of block-oriented nonlinear systems starting from linear approximations: a survey ⋮ A unified framework for the identification of a general class of multivariable nonlinear block‐structured systems ⋮ Iterative selection of unknown weights in direct weight optimization identification ⋮ Recursive and iterative least squares parameter estimation algorithms for observability canonical state space systems ⋮ Kalman state filtering based least squares iterative parameter estimation for observer canonical state space systems using decomposition ⋮ Symmetric tensor decomposition by an iterative eigendecomposition algorithm ⋮ Computationally efficient identification of continuous-time Lur'e-type systems with stability guarantees ⋮ Decoupling multivariate polynomials: interconnections between tensorizations ⋮ Decoupling Multivariate Polynomials Using First-Order Information and Tensor Decompositions ⋮ Stealth identification strategy for closed loop system structure

Cites Work

• Unnamed Item
• Parametric identification of structured nonlinear systems
• Stability analysis of networked control systems subject to packet-dropouts and finite-level quantization
• Identification of nonlinear systems using polynomial nonlinear state space models
• Measurement and identification of nonlinear systems consisting of linear dynamic blocks and one static nonlinearity
• Subspace-based multivariable system identification from frequency response data

This page was built for publication: Identification of systems with localised nonlinearity: from state-space to block-structured models