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- W3039353034 abstract "Abstract In this paper, an event-triggered strategy and an optimality scheme are presented to design an output-feedback controller for nonlinear discrete-time systems. Firstly, owing to the unavailability of the system states, a coordinate transformation is executed instead of state observers. Then, an action neural network and a specified critic neural network are presented to obtain the optimal controller and estimate the novel long-term cost function, respectively. The adaptation laws are designed on the basis of the gradient descent rule and event-triggered mechanism. The control signals are transmitted only when the event is triggered. Based on the Lyapunov analysis theory, the stability and the tracking performance of the closed-loop system are proven. The effectiveness of the proposed strategy is verified via simulation examples." @default.
- W3039353034 created "2020-07-10" @default.
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- W3039353034 date "2020-11-01" @default.
- W3039353034 modified "2023-09-27" @default.
- W3039353034 title "Event-based optimal output-feedback control of nonlinear discrete-time systems" @default.
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- W3039353034 doi "https://doi.org/10.1016/j.ins.2020.05.098" @default.
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