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Online identification methods for a class of Hammerstein nonlinear systems using the adaptive particle filtering.
- Source :
-
Chaos, Solitons & Fractals . Sep2024, Vol. 186, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Hammerstein structure is commonly used for describing nonlinear dynamic characteristics, and its identification is a basic premise of nonlinear system analysis and control. This paper investigates online identification methods for a class of Hammerstein nonlinear systems, which consists of a nonlinear memoryless element followed by a linear output-error subsystem. The unmeasurable noise-free output of the linear subsystem makes the model parameters cannot be directly estimated by traditional identification methods. To address this difficulty, by using a series of weighted particles to adaptively approximate the posterior probability density function of the unmeasurable noise-free output, this paper proposes a particle filter-based stochastic gradient algorithm. Moreover, to enhance the data utilization and estimation accuracy, a particle filter-based multi-innovation stochastic gradient algorithm is developed through the innovation expansion technique. The simulation results demonstrate that compared with the existing benchmark algorithms, the proposed algorithms need a little more computational time due to the introduction of the adaptive particle filter, but they have the improved identification accuracies. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09600779
- Volume :
- 186
- Database :
- Academic Search Index
- Journal :
- Chaos, Solitons & Fractals
- Publication Type :
- Periodical
- Accession number :
- 178885338
- Full Text :
- https://doi.org/10.1016/j.chaos.2024.115181