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Indexed by:会议论文

Date of Publication:2014-01-01

Included Journals:CPCI-S

Page Number:3181-3185

Key Words:non-uniform sampling; state-space model identification; QR decomposition; recursive least-squares

Abstract:A recursive least-squares (LS) state-space identification method based on the QR decomposition is proposed for non-uniformly sampled-data systems. Both cases of measuring all states and only the output(s) are considered for model identification. For the case of state measurement, a QR decomposition-based recursive LS (QRD-RLS) identification algorithm is given to estimate the state matrices. For the case of only output measurement, another identification algorithm is developed by combining the QRD-RLS approach with a hierarchical identification strategy. Both algorithms can guarantee fast convergence rate with low computation complexity. An illustrative example is shown to demonstrate the effectiveness of the proposed methods.