Finite-Time Adaptive Neural Prescribed Performance Control for High-Order Nonlinearly Parameterized Switched Systems With Unmodeled Dynamics and Input Quantization
This study focuses on the adaptive prescribed-time neural control for a class of high-order switched systems with nonlinear parameterization in presence of unmodeled dynamics and quantized input. Different from the existing results on finite-time control on basis of adding a power integrator techniq...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10378685/ |
| _version_ | 1827383311319695360 |
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| author | Jiao-Jun Zhang Yong-Hua Zhou Qi-Ming Sun |
| author_facet | Jiao-Jun Zhang Yong-Hua Zhou Qi-Ming Sun |
| author_sort | Jiao-Jun Zhang |
| collection | DOAJ |
| description | This study focuses on the adaptive prescribed-time neural control for a class of high-order switched systems with nonlinear parameterization in presence of unmodeled dynamics and quantized input. Different from the existing results on finite-time control on basis of adding a power integrator technique, the controller construction and stability analysis are simplified, and the tracking error remains within a set range over any prescribed time. Under the frame of backstepping design, a state feedback controller is designed. During the controller design procedure, Radial basis function (RBF) neural networks with minimal learning parameters are employed to identify the unknown compounded nonlinear functions, and the control input is quantized. Based on Lyapunov stability theory, the closed-loop system’s signals are all assured to be semi-globally uniformly bounded (SGUB), and the tracking error is kept inside a prescribed zone at a finite time. Finally, a numerical simulation is provided to demonstrate the viability and efficacy of the control strategy. |
| first_indexed | 2024-03-08T14:39:55Z |
| format | Article |
| id | doaj.art-a005f9e69f124dc1ae2dc2163ee41e3e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T14:39:55Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a005f9e69f124dc1ae2dc2163ee41e3e2024-01-12T00:00:34ZengIEEEIEEE Access2169-35362024-01-01124618463010.1109/ACCESS.2023.334845510378685Finite-Time Adaptive Neural Prescribed Performance Control for High-Order Nonlinearly Parameterized Switched Systems With Unmodeled Dynamics and Input QuantizationJiao-Jun Zhang0https://orcid.org/0000-0002-5674-6106Yong-Hua Zhou1Qi-Ming Sun2https://orcid.org/0000-0002-5010-4903Department of Mathematical Sciences, Zhejiang Sci-Tech University, Hangzhou, ChinaDepartment of Mathematical Sciences, Zhejiang Sci-Tech University, Hangzhou, ChinaCollege of Information Science and Technology, Nanjing Forestry University, Nanjing, ChinaThis study focuses on the adaptive prescribed-time neural control for a class of high-order switched systems with nonlinear parameterization in presence of unmodeled dynamics and quantized input. Different from the existing results on finite-time control on basis of adding a power integrator technique, the controller construction and stability analysis are simplified, and the tracking error remains within a set range over any prescribed time. Under the frame of backstepping design, a state feedback controller is designed. During the controller design procedure, Radial basis function (RBF) neural networks with minimal learning parameters are employed to identify the unknown compounded nonlinear functions, and the control input is quantized. Based on Lyapunov stability theory, the closed-loop system’s signals are all assured to be semi-globally uniformly bounded (SGUB), and the tracking error is kept inside a prescribed zone at a finite time. Finally, a numerical simulation is provided to demonstrate the viability and efficacy of the control strategy.https://ieeexplore.ieee.org/document/10378685/High-order nonlinear systemsnonlinear parameterizationswitched systemsprescribed-time controlunmodeled dynamicsinput quantization |
| spellingShingle | Jiao-Jun Zhang Yong-Hua Zhou Qi-Ming Sun Finite-Time Adaptive Neural Prescribed Performance Control for High-Order Nonlinearly Parameterized Switched Systems With Unmodeled Dynamics and Input Quantization IEEE Access High-order nonlinear systems nonlinear parameterization switched systems prescribed-time control unmodeled dynamics input quantization |
| title | Finite-Time Adaptive Neural Prescribed Performance Control for High-Order Nonlinearly Parameterized Switched Systems With Unmodeled Dynamics and Input Quantization |
| title_full | Finite-Time Adaptive Neural Prescribed Performance Control for High-Order Nonlinearly Parameterized Switched Systems With Unmodeled Dynamics and Input Quantization |
| title_fullStr | Finite-Time Adaptive Neural Prescribed Performance Control for High-Order Nonlinearly Parameterized Switched Systems With Unmodeled Dynamics and Input Quantization |
| title_full_unstemmed | Finite-Time Adaptive Neural Prescribed Performance Control for High-Order Nonlinearly Parameterized Switched Systems With Unmodeled Dynamics and Input Quantization |
| title_short | Finite-Time Adaptive Neural Prescribed Performance Control for High-Order Nonlinearly Parameterized Switched Systems With Unmodeled Dynamics and Input Quantization |
| title_sort | finite time adaptive neural prescribed performance control for high order nonlinearly parameterized switched systems with unmodeled dynamics and input quantization |
| topic | High-order nonlinear systems nonlinear parameterization switched systems prescribed-time control unmodeled dynamics input quantization |
| url | https://ieeexplore.ieee.org/document/10378685/ |
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