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- W2726520282 abstract "Abstract This paper is concerned with the event-triggered data-driven control problem for nonlinear discrete-time systems. An event-based data-driven model-free adaptive controller design algorithm together with constructing an adaptive event-trigger condition is developed. Different from the existing data-driven model-free adaptive control approach, an aperiodic neural network weight update law is introduced to estimate the controller parameters, and the event-trigger mechanism is activated only if the event-trigger error exceeds the threshold. Furthermore, by combining the equivalent-dynamic-linearization technique with the Lyapunov method, it is proved that both the closed-loop control system and the weight estimation error are ultimately bounded. Finally, two simulation examples are provided to demonstrate the effectiveness of the derived method." @default.
- W2726520282 created "2017-07-14" @default.
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- W2726520282 date "2018-01-01" @default.
- W2726520282 modified "2023-10-16" @default.
- W2726520282 title "Neural network-based event-triggered MFAC for nonlinear discrete-time processes" @default.
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- W2726520282 doi "https://doi.org/10.1016/j.neucom.2017.07.008" @default.
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