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Adaptive neural network terminal sliding mode tracking control for uncertain nonlinear systems with time-varying state constraints

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Publicado en:Measurement and Control vol. 58, no. 5 (May 2025), p. 553
Autor principal: Dao-gen Jiang
Otros Autores: Long-jin, Lv, Sun-hao, Song, Jia-hao, Li
Publicado:
Sage Publications Ltd.
Materias:
Asymmetry
Control theory
Neural networks
Tracking control
Mode tracking
Sliding mode control
Algorithms
Nonlinear systems
Subsystems
Nonlinear control
Liapunov functions
Control methods
Tracking errors
Constraints
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Resumen: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.
ISSN:0020-2940
2051-8730
DOI:10.1177/00202940241279360
Fuente:Advanced Technologies & Aerospace Database