Data Filtering-Based Maximum Likelihood Gradient-Based Iterative Algorithm for Input Nonlinear Box–Jenkins Systems with Saturation Nonlinearity
Saturation nonlinearity exists widely in various practical control systems. Modeling and parameter estimation of systems with saturation nonlinearity are of great importance for analyzing their characteristics and controller design. This paper focuses on the identification issue of the input nonline...
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| Published in | Circuits, systems, and signal processing Vol. 43; no. 11; pp. 6874 - 6910 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.11.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0278-081X 1531-5878 |
| DOI | 10.1007/s00034-024-02777-0 |
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| Abstract | Saturation nonlinearity exists widely in various practical control systems. Modeling and parameter estimation of systems with saturation nonlinearity are of great importance for analyzing their characteristics and controller design. This paper focuses on the identification issue of the input nonlinear Box–Jenkins systems with saturation nonlinearity. The input saturation nonlinearity is presented as a linear parametric expression through the application of a switching function, then the identification model of the system is derived by using the key term separation technique. Based on this model and the data filtering technique, the filtering identification model of the system is given by changing the system structure without changing the relationship between the input and output, which can reduce the interference of the colored noise and improve the identification accuracy. Then a data filtering-based maximum likelihood gradient-based iterative algorithm is proposed to estimate the unknown parameters. The maximum likelihood gradient-based iterative algorithm is provided for comparison. The feasibility and superiority of the proposed approach are emphasized by a simulation example. |
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| AbstractList | Saturation nonlinearity exists widely in various practical control systems. Modeling and parameter estimation of systems with saturation nonlinearity are of great importance for analyzing their characteristics and controller design. This paper focuses on the identification issue of the input nonlinear Box–Jenkins systems with saturation nonlinearity. The input saturation nonlinearity is presented as a linear parametric expression through the application of a switching function, then the identification model of the system is derived by using the key term separation technique. Based on this model and the data filtering technique, the filtering identification model of the system is given by changing the system structure without changing the relationship between the input and output, which can reduce the interference of the colored noise and improve the identification accuracy. Then a data filtering-based maximum likelihood gradient-based iterative algorithm is proposed to estimate the unknown parameters. The maximum likelihood gradient-based iterative algorithm is provided for comparison. The feasibility and superiority of the proposed approach are emphasized by a simulation example. |
| Author | Li, Meihang Fan, Yamin Liu, Ximei |
| Author_xml | – sequence: 1 givenname: Yamin orcidid: 0000-0003-0724-3550 surname: Fan fullname: Fan, Yamin email: yaminfans@126.com organization: School of Electronic Information Engineering, Taiyuan University of Science and Technology – sequence: 2 givenname: Ximei surname: Liu fullname: Liu, Ximei email: liuximei@qust.edu.cn organization: College of Automation and Electronic Engineering, Qingdao University of Science and Technology – sequence: 3 givenname: Meihang surname: Li fullname: Li, Meihang organization: College of Automation and Electronic Engineering, Qingdao University of Science and Technology |
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| CitedBy_id | crossref_primary_10_1016_j_dsp_2024_104951 crossref_primary_10_1002_acs_3963 crossref_primary_10_1007_s00034_025_03068_y crossref_primary_10_1177_09596518241309132 crossref_primary_10_1007_s00034_025_03016_w crossref_primary_10_1016_j_oceaneng_2025_120674 crossref_primary_10_1002_oca_3257 crossref_primary_10_1002_oca_3279 crossref_primary_10_1038_s41598_025_89969_z crossref_primary_10_1016_j_eswa_2025_126464 |
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| Keywords | Box–Jenkins system Parameter estimation Iterative identification Maximum likelihood Saturation nonlinearity Data filtering |
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| Snippet | Saturation nonlinearity exists widely in various practical control systems. Modeling and parameter estimation of systems with saturation nonlinearity are of... |
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| SubjectTerms | Algorithms Circuits and Systems Control systems design Electrical Engineering Electronics and Microelectronics Engineering Filtration Instrumentation Iterative algorithms Maximum likelihood estimates Nonlinear control Nonlinear systems Nonlinearity Parameter estimation Parameter identification Signal,Image and Speech Processing |
| Title | Data Filtering-Based Maximum Likelihood Gradient-Based Iterative Algorithm for Input Nonlinear Box–Jenkins Systems with Saturation Nonlinearity |
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