Adaptive neural network terminal sliding mode tracking control for uncertain nonlinear systems with time-varying state constraints
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| Publicado no: | Measurement and Control vol. 58, no. 5 (May 2025), p. 553 |
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| Autor principal: | |
| Outros Autores: | , , |
| Publicado em: |
Sage Publications Ltd.
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| Acesso em linha: | Citation/Abstract Full text outside of ProQuest |
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| 003 | UK-CbPIL | ||
| 022 | |a 0020-2940 | ||
| 022 | |a 2051-8730 | ||
| 024 | 7 | |a 10.1177/00202940241279360 |2 doi | |
| 035 | |a 3201689778 | ||
| 045 | 2 | |b d20250501 |b d20250531 | |
| 100 | 1 | |a Dao-gen Jiang |u Information and Intelligent Engineering Department, Ningbo City College of Vocational Technology, Ningbo, China | |
| 245 | 1 | |a Adaptive neural network terminal sliding mode tracking control for uncertain nonlinear systems with time-varying state constraints | |
| 260 | |b Sage Publications Ltd. |c May 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Exploring a novel adaptive asymmetric sliding mode control methodology with time-varying state constraints (TVSCs), we address trajectory tracking issues in uncertain nonlinear systems. The asymmetric barrier Lyapunov functions (ABLFs) and neural networks is employed within each subsystem’s virtual control design process using back-stepping control (BSC) method. This ensures the imposition of TVSCs and effectively addresses challenges posed by system uncertainties. Additionally, to enhance the convergence of tracking deviations within small zero neighborhoods, a nonsingular integral terminal sliding mode control (NITSMC) method is incorporated into the actual control algorithm design. This method illustrates that, the system states consistently stay within the specified boundaries, tracking errors rapidly converge to a confined range. All signals within the system remain bounded. Simulation findings affirm the efficacy of the suggested control strategy. | |
| 653 | |a Asymmetry | ||
| 653 | |a Control theory | ||
| 653 | |a Neural networks | ||
| 653 | |a Tracking control | ||
| 653 | |a Mode tracking | ||
| 653 | |a Sliding mode control | ||
| 653 | |a Algorithms | ||
| 653 | |a Nonlinear systems | ||
| 653 | |a Subsystems | ||
| 653 | |a Nonlinear control | ||
| 653 | |a Liapunov functions | ||
| 653 | |a Control methods | ||
| 653 | |a Tracking errors | ||
| 653 | |a Constraints | ||
| 700 | 1 | |a Long-jin, Lv |u Economics and Information College, Ningbo University of Finance and Economics, Ningbo, China | |
| 700 | 1 | |a Sun-hao, Song |u Information and Intelligent Engineering Department, Ningbo City College of Vocational Technology, Ningbo, China | |
| 700 | 1 | |a Jia-hao, Li |u Information and Intelligent Engineering Department, Ningbo City College of Vocational Technology, Ningbo, China | |
| 773 | 0 | |t Measurement and Control |g vol. 58, no. 5 (May 2025), p. 553 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3201689778/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://journals.sagepub.com/doi/10.1177/00202940241279360 |