Adaptive event‐triggered tracking control for a class of stochastic nonlinear systems with full‐state constraints

This paper proposes an adaptive event trigger‐based sample‐and‐hold tracking control scheme for a class of strict‐feedback nonlinear stochastic systems with full‐state constraints. By introducing a tan‐type stochastic Barrier Lyapunov function (SBLF) combined with radial basis function neural networ...

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Bibliographic Details
Published in:Asian journal of control 2023-03, Vol.25 (2), p.1202-1215
Main Authors: Lu, Hang, Jiang, Yan, Luo, Shixian
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive sampling
backstepping
barrier Lyapunov function
Control systems design
event‐triggered control
Liapunov functions
Neural networks
nonlinear stochastic systems
Nonlinear systems
Radial basis function
RBFNNs
Stochastic systems
Tracking control
Tracking errors
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Summary:This paper proposes an adaptive event trigger‐based sample‐and‐hold tracking control scheme for a class of strict‐feedback nonlinear stochastic systems with full‐state constraints. By introducing a tan‐type stochastic Barrier Lyapunov function (SBLF) combined with radial basis function neural networks (RBFNNs), which is used to approximate the nonlinear functions in backstepping procedures, an adaptive event‐triggered controller is designed. It is shown with stochastic stability theory that all the states cannot violate their constraints, and Zeno behavior is excluded almost surely. Meanwhile, all the signals of the closed‐loop systems are bounded almost surely and the tracking error converges to an arbitrary small compact set in the fourth‐moment sense. A simulation example is given to show the effectiveness of the control scheme.
ISSN:1561-8625
1934-6093
DOI:10.1002/asjc.2902