CEC Faculty Books and Book Chapters

Indirect Continuous-Time LPV System Identification Through a Downsampled Subspace Approach

Title

Indirect Continuous-Time LPV System Identification Through a Downsampled Subspace Approach

Files

Document Type

Book Chapter

Book Title

Linear Parameter-Varying System Identification, New Developments and Trends (Advanced Series in Electrical and Computer Engineering)

Description

The successive approximation Linear Parameter Varying systems subspace identification algorithm for discrete-time systems is based on a convergent sequence of linear time invariant deterministic-stochastic state-space approximations. In this chapter, this method is modified to cope with continuous-time LPV state-space models. To do this, the LPV system is discretised, the discrete-time model is identified by the successive approximations algorithm and then converted to a continuous-time model. Since affine dependence is preserved only for fast sampling, a subspace downsampling approach is used to estimate the linear time invariant deterministic-stochastic state-space approximations. A second order simulation example, with complex poles, illustrates the effectiveness of the new algorithm.

ISBN

978-9814355445

Publication Date

2011

Publisher

World Scientific Publishing

City

Singapore

Disciplines

Computer Sciences

Comments

pp. 231-258

Indirect Continuous-Time LPV System Identification Through a Downsampled Subspace Approach
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