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Event-triggered fixed-time adaptive neural tracking control for stochastic non-triangular structure nonlinear systems

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Abstract

This article discusses the problem of event-triggered fixed-time control for a class of stochastic non-triangular structure nonlinear systems. The Mean Value Theorem is employed to transform the stochastic non-triangular structure nonlinear systems into the equivalent systems with affine structure. The unknown nonlinear functions are approximated by the radial basis function neural networks. Combining with the fixed-time stable theory, backstepping technique and event-triggered control technique, a novel event-triggered fixed-time adaptive controller is designed. The results manifest that the whole signals in the closed-loop system are bounded in probability. Meanwhile, the tracking error converges to a small residual set within a fixed-time interval. Two simulations are included to illustrate the effectiveness of the proposed method.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61603003, 62172135).

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Authors and Affiliations

  1. School of Mathematics, Hefei University of Technology, Hefei, 230601, China

    Yangang Yao, Jieqing Tan & Xu Zhang

  2. School of Computer and Information, Anqing Normal University, Anqing, 246011, China

    Jian Wu

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  1. Yangang Yao
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Correspondence to Jieqing Tan.

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Yao, Y., Tan, J., Wu, J. et al. Event-triggered fixed-time adaptive neural tracking control for stochastic non-triangular structure nonlinear systems. Neural Comput & Applic 33, 15887–15899 (2021). https://doi.org/10.1007/s00521-021-06210-4

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