BLF-based neural dynamic surface control for stochastic nonlinear systems with time delays and full-state constraints

This paper investigates the adaptive neural tracking control problem for a class of stochastic nonlinear systems with time delays and full-state constraints in a unified framework for the first time. The time-delay terms of the controlled systems are compensated by novel Lyapunov-Krasovskii function...

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Bibliographic Details
Published in:International journal of control 2024-05, Vol.97 (5), p.982-998
Main Author: Shu, Yanjun
Format: Article
Language:English
Subjects:
Adaptive control
barrier Lyapunov function
Closed loops
Constraints
DSC
full-state constraint
Liapunov functions
Lyapunov-Krasovskii functionals
Neural networks
Nonlinear control
Nonlinear systems
Stability analysis
Stochastic nonlinear systems
Stochastic systems
Tracking control
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Summary:This paper investigates the adaptive neural tracking control problem for a class of stochastic nonlinear systems with time delays and full-state constraints in a unified framework for the first time. The time-delay terms of the controlled systems are compensated by novel Lyapunov-Krasovskii functionals. The asymmetric barrier Lyapunov function (BLF) is adopted to guarantee that the full states are always restricted within prescribed constraints. RBF neural networks are utilised to approximate the lumped unknown functions in the design process. Furthermore, the dynamic surface control (DSC) technique is employed to simplify the process of control design significantly. Stability analysis shows all closed-loop signals are SGUUB, and full-state constraints are not violated. Finally, simulation results confirm the effectiveness of the proposed control scheme.
ISSN:0020-7179
1366-5820
DOI:10.1080/00207179.2023.2188432