Identification of Approximative Nonlinear State-Space Models by Subspace Methods

Andreas Schrempf; Vincent Verdult

Identification of Approximative Nonlinear State-Space Models by Subspace Methods

Andreas Schrempf, Vincent Verdult

Institute of Design and Control of Mechatronical Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

A subspace identification algorithm for state-affine state-space systems which allows to approximate nonlinear systems arbitrarily well is derived. The proposed algorithm depends on an approximation step where a detailed approximation error analysis is provided. A special case is presented in which this approximation error vanishes. To tackle higher-order systems and ill-posed problems a regularized kernel method is proposed. The algorithm is evaluated by a simulation study.

Original language	English
Title of host publication	16th IFAC World Congress
Number of pages	6
Publication status	Published - Jul 2005

Fields of science

202 Electrical Engineering, Electronics, Information Engineering
202027 Mechatronics
202034 Control engineering
203027 Internal combustion engines
206001 Biomedical engineering
206002 Electro-medical engineering
207109 Pollutant emission

Cite this

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abstract = "A subspace identification algorithm for state-affine state-space systems which allows to approximate nonlinear systems arbitrarily well is derived. The proposed algorithm depends on an approximation step where a detailed approximation error analysis is provided. A special case is presented in which this approximation error vanishes. To tackle higher-order systems and ill-posed problems a regularized kernel method is proposed. The algorithm is evaluated by a simulation study.",

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