Dynamic event-triggered delay compensation control for networked predictive control systems with random delay

Zhang, Ji

doi:10.1038/s41598-023-46753-1

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…When the neural network is applied to information transmission, it is necessary to sample the data from time to time, although it is possible to transmit all the data resources, sometimes unnecessary data is also transmitted, which can waste the communication resources.But event-triggered control can increase efficiency while guaranteeing performance 30 , 31 . Most of the studies on controlling the periodic execution of events for signals in communication transmission systems has been through ZOH or time-event-triggered schemes 32 , 33 .…”

Section: Introductionmentioning

confidence: 99%

H∞ state estimation of continuous-time neural networks with uncertainties

Li,

Chen,

et al. 2024

Sci Rep

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Abstract$$H_{\infty }$$ H ∞ state estimation is addressed for continuous-time neural networks in the paper. The norm-bounded uncertainties are considered in communication neural networks. For the considered neural networks with uncertainties, a reduced-order $$H_{\infty }$$ H ∞ state estimator is designed, which makes that the error dynamics is exponentially stable and has weighted $$H_{\infty }$$ H ∞ performance index by Lyapunov function method. Moreover, it is also given the devised method of the reduced-order $$H_{\infty }$$ H ∞ state estimator. Then, considering that sampling the output y(t) of the neural network at every moment will result in waste of excess resources, the event-triggered sampling strategy is used to solve the oversampling problem. In addition, a devised method is also given for the event-triggered reduced-order $$H_{\infty }$$ H ∞ state estimator. Finally, by the well-known Tunnel Diode Circuit example, it shows that a lower order state estimator can be designed under the premise of maintaining the same weighted $$H_{\infty }$$ H ∞ performance index, and using the event-triggered sampling method can reduce the computational and time costs and save communication resources.

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