Adaptive NN control for nominal backstepping form with periodically time‐varying and nonlinearly parameterized switching functions
Abstract In this paper, the prescribed tracking performance control problem is addressed for uncertain nonlinear systems with unknown periodically time‐varying parameters and arbitrary switching signal. By utilizing radial basis function neural network and fourier series expansion, an approximator i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-11-01
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| Series: | IET Control Theory & Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cth2.12517 |
| _version_ | 1797626700953026560 |
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| author | Xiaoli Yang Jing Li Shuzhi Sam Ge Xiaobo Li |
| author_facet | Xiaoli Yang Jing Li Shuzhi Sam Ge Xiaobo Li |
| author_sort | Xiaoli Yang |
| collection | DOAJ |
| description | Abstract In this paper, the prescribed tracking performance control problem is addressed for uncertain nonlinear systems with unknown periodically time‐varying parameters and arbitrary switching signal. By utilizing radial basis function neural network and fourier series expansion, an approximator is developed to overcome the difficulty of identifying unknown periodically time‐varying and nonlinearly parameterized functions. To achieve the ideal tracking control performance and eliminate the influence of filtering error, a novel command filter‐based adaptive neural network prescribed tracking performance controller is designed by introducing a filtering compensation mechanism. Differently from the standard Backstepping technique, the proposed control scheme eliminates the “explosion of complexity” problem and relaxes the constraint condition on the reference signal. And then, it is warranted that the closed‐loop system is semi‐globally ultimately uniformly bounded and the tracking error is always limited to the specified region bounded by the performance functions. Three simulation examples are used to demonstrate the feasibility of the developed technique in this paper. |
| first_indexed | 2024-03-11T10:13:57Z |
| format | Article |
| id | doaj.art-92c8e29b52cf4d5e9762dac74de7e020 |
| institution | Directory Open Access Journal |
| issn | 1751-8644 1751-8652 |
| language | English |
| last_indexed | 2024-03-11T10:13:57Z |
| publishDate | 2023-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Control Theory & Applications |
| spelling | doaj.art-92c8e29b52cf4d5e9762dac74de7e0202023-11-16T11:09:24ZengWileyIET Control Theory & Applications1751-86441751-86522023-11-0117172353236810.1049/cth2.12517Adaptive NN control for nominal backstepping form with periodically time‐varying and nonlinearly parameterized switching functionsXiaoli Yang0Jing Li1Shuzhi Sam Ge2Xiaobo Li3School of Mathematics and Statistics Xidian University Xi'an ChinaSchool of Mathematics and Statistics Xidian University Xi'an ChinaThe Department of Electrical and Computer Engineering National University of Singapore SingaporeSchool of Mathematics and Information Science Baoji University of Arts and Sciences Baoji ChinaAbstract In this paper, the prescribed tracking performance control problem is addressed for uncertain nonlinear systems with unknown periodically time‐varying parameters and arbitrary switching signal. By utilizing radial basis function neural network and fourier series expansion, an approximator is developed to overcome the difficulty of identifying unknown periodically time‐varying and nonlinearly parameterized functions. To achieve the ideal tracking control performance and eliminate the influence of filtering error, a novel command filter‐based adaptive neural network prescribed tracking performance controller is designed by introducing a filtering compensation mechanism. Differently from the standard Backstepping technique, the proposed control scheme eliminates the “explosion of complexity” problem and relaxes the constraint condition on the reference signal. And then, it is warranted that the closed‐loop system is semi‐globally ultimately uniformly bounded and the tracking error is always limited to the specified region bounded by the performance functions. Three simulation examples are used to demonstrate the feasibility of the developed technique in this paper.https://doi.org/10.1049/cth2.12517command filterprescribed tracking performanceperiodically time‐varying parametersradial basis function neural networksuncertain switching nonlinear systems |
| spellingShingle | Xiaoli Yang Jing Li Shuzhi Sam Ge Xiaobo Li Adaptive NN control for nominal backstepping form with periodically time‐varying and nonlinearly parameterized switching functions IET Control Theory & Applications command filter prescribed tracking performance periodically time‐varying parameters radial basis function neural networks uncertain switching nonlinear systems |
| title | Adaptive NN control for nominal backstepping form with periodically time‐varying and nonlinearly parameterized switching functions |
| title_full | Adaptive NN control for nominal backstepping form with periodically time‐varying and nonlinearly parameterized switching functions |
| title_fullStr | Adaptive NN control for nominal backstepping form with periodically time‐varying and nonlinearly parameterized switching functions |
| title_full_unstemmed | Adaptive NN control for nominal backstepping form with periodically time‐varying and nonlinearly parameterized switching functions |
| title_short | Adaptive NN control for nominal backstepping form with periodically time‐varying and nonlinearly parameterized switching functions |
| title_sort | adaptive nn control for nominal backstepping form with periodically time varying and nonlinearly parameterized switching functions |
| topic | command filter prescribed tracking performance periodically time‐varying parameters radial basis function neural networks uncertain switching nonlinear systems |
| url | https://doi.org/10.1049/cth2.12517 |
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