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• W4317659239 abstract "In this chapter, the self-learning optimal regulationOptimal regulation for discrete-time nonaffine nonlinear systemsNonaffine nonlinear systems under event-driven formulation is investigated. An event-based adaptive critic algorithm is developed with a convergence discussion of the iterative process. The input-to-state stabilityInput-to-state stability (ISS) analysis for the present nonlinear plant is established. Then, a suitable triggering conditionTriggering condition is proved to ensure the ISS of the controlled system. An iterative dual heuristic dynamic programmingDual heuristic dynamic programming (DHP) strategy is adopted to implement the event-driven framework. As a special case discussion, the event-driven iterative adaptive criticIterative adaptive critic design of affine nonlinear systemsAffine nonlinear systems is also presented. Additionally, the mixed-driven control framework is highlighted involving data and event considerations and is integrated with the iterative adaptive criticIterative adaptive critic algorithm. Simulation examples are carried out to demonstrate the applicability of the constructed event-driven method, respectively, implemented via DHP, heuristic dynamic programmingHeuristic dynamic programming, and neural dynamic programmingNeural dynamic programming. Remarkably, compared with the traditional DHP algorithm, the event-based algorithm is able to substantially reduce the updating times of the control input while still maintaining an impressive performance." @default.
• W4317659239 created "2023-01-21" @default.
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• W4317659239 date "2023-01-01" @default.
• W4317659239 modified "2023-10-01" @default.
• W4317659239 title "Self-Learning Optimal Regulation with Event-Driven Iterative Adaptive Critic" @default.
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• W4317659239 doi "https://doi.org/10.1007/978-981-19-7291-1_3" @default.
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