Journal
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
Volume 19, Issue 3, Pages 656-663Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCST.2010.2052257
Keywords
B-splines basis functions; block least mean squares (LMS); normalized least mean squares (LMS); parameter estimation; recursive least squares (RLS); system identification; time variation
Funding
- Engineering and Physical Sciences Research Council (EPSRC), U.K.
- European Research Council (ERC)
- Engineering and Physical Sciences Research Council [EP/H00453X/1, EP/I011056/1, EP/G042209/1] Funding Source: researchfish
- EPSRC [EP/H00453X/1, EP/I011056/1, EP/G042209/1] Funding Source: UKRI
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This brief introduces a new parametric modelling and identification method for linear time-varying systems using a block least mean square (LMS) approach where the time-varying parameters are approximated using multi-wavelet basis functions. This approach can be applied to track rapidly or even sharply varying processes and is developed by combining wavelet approximation theory with a block LMS algorithm. Numerical examples are provided to show the effectiveness of the proposed method for dealing with severely nonstationary processes. Application of the proposed approach to a real mechanical system indicates better tracking capability of the multi-wavelet basis function algorithm compared with the normalized least squares or recursive least squares routines.
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