Finite-Time Adaptive Neural Control Scheme for Uncertain High-Order Systems with Input Nonlinearities and Unmodeled Dynamics
This paper proposes a novel finite-time adaptive neural control method for a class of high-order nonlinear systems with high powers in the presence of dead zone input nonlinearities and unmodeled dynamics. By utilizing prescribed performance functions and radial basis function neural networks, the t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/18/2835 |
| _version_ | 1797489127797555200 |
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| author | Hantong Mei Hanqiao Huang Yunhe Guo Guan Huang Feihong Xu |
| author_facet | Hantong Mei Hanqiao Huang Yunhe Guo Guan Huang Feihong Xu |
| author_sort | Hantong Mei |
| collection | DOAJ |
| description | This paper proposes a novel finite-time adaptive neural control method for a class of high-order nonlinear systems with high powers in the presence of dead zone input nonlinearities and unmodeled dynamics. By utilizing prescribed performance functions and radial basis function neural networks, the tracking error and state errors are limited within the preassigned range in a finite time, which can be specified by the designer in advance according to the chosen the parameters of the novel prescribed performance functions. Nonlinear transformed error surfaces are designed to counteract the effects of dead zone input nonlinearities in nonlinear high-order systems with unknown system nonlinearities and unmodeled dynamics. Based on the Lyapunov theorem, the tracking errors are proven to converge into a preassigned set in a finite time previously specified by the novel prescribed performance function. Finally, simulation results demonstrate the effectiveness of the proposed method. |
| first_indexed | 2024-03-10T00:12:04Z |
| format | Article |
| id | doaj.art-638e980cc27d4478a1c16c5feed58fd2 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T00:12:04Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-638e980cc27d4478a1c16c5feed58fd22023-11-23T15:57:27ZengMDPI AGElectronics2079-92922022-09-011118283510.3390/electronics11182835Finite-Time Adaptive Neural Control Scheme for Uncertain High-Order Systems with Input Nonlinearities and Unmodeled DynamicsHantong Mei0Hanqiao Huang1Yunhe Guo2Guan Huang3Feihong Xu4School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaShanghai Electro-Mechanical Engineering Institute, Shanghai 201100, ChinaElectronics Standardization Institute, Beijing 100007, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaThis paper proposes a novel finite-time adaptive neural control method for a class of high-order nonlinear systems with high powers in the presence of dead zone input nonlinearities and unmodeled dynamics. By utilizing prescribed performance functions and radial basis function neural networks, the tracking error and state errors are limited within the preassigned range in a finite time, which can be specified by the designer in advance according to the chosen the parameters of the novel prescribed performance functions. Nonlinear transformed error surfaces are designed to counteract the effects of dead zone input nonlinearities in nonlinear high-order systems with unknown system nonlinearities and unmodeled dynamics. Based on the Lyapunov theorem, the tracking errors are proven to converge into a preassigned set in a finite time previously specified by the novel prescribed performance function. Finally, simulation results demonstrate the effectiveness of the proposed method.https://www.mdpi.com/2079-9292/11/18/2835finite-time trackingprescribed performanceinput nonlinearitiesunmodeled dynamicsneural networks |
| spellingShingle | Hantong Mei Hanqiao Huang Yunhe Guo Guan Huang Feihong Xu Finite-Time Adaptive Neural Control Scheme for Uncertain High-Order Systems with Input Nonlinearities and Unmodeled Dynamics Electronics finite-time tracking prescribed performance input nonlinearities unmodeled dynamics neural networks |
| title | Finite-Time Adaptive Neural Control Scheme for Uncertain High-Order Systems with Input Nonlinearities and Unmodeled Dynamics |
| title_full | Finite-Time Adaptive Neural Control Scheme for Uncertain High-Order Systems with Input Nonlinearities and Unmodeled Dynamics |
| title_fullStr | Finite-Time Adaptive Neural Control Scheme for Uncertain High-Order Systems with Input Nonlinearities and Unmodeled Dynamics |
| title_full_unstemmed | Finite-Time Adaptive Neural Control Scheme for Uncertain High-Order Systems with Input Nonlinearities and Unmodeled Dynamics |
| title_short | Finite-Time Adaptive Neural Control Scheme for Uncertain High-Order Systems with Input Nonlinearities and Unmodeled Dynamics |
| title_sort | finite time adaptive neural control scheme for uncertain high order systems with input nonlinearities and unmodeled dynamics |
| topic | finite-time tracking prescribed performance input nonlinearities unmodeled dynamics neural networks |
| url | https://www.mdpi.com/2079-9292/11/18/2835 |
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