The self-learning optimal regulation for discrete-time nonlinear systems under event-driven formulation is investigated. An event-based adaptive critic algorithm is developed with convergence discussion of the iterative process. The input-to-state stability (ISS) analysis for the present nonlinear plant is established. Then, a suitable triggering condition is proved to ensure the ISS of the controlled system. An iterative dual heuristic dynamic programming (DHP) strategy is adopted to implement the event-driven framework. Simulation examples are carried out to demonstrate the applicability of the constructed method. Compared with the traditional DHP algorithm, the even-based algorithm is able to substantially reduce the updating times of the control input, while still maintaining an impressive performance.

中文翻译：

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事件驱动公式下离散时间非线性系统的自学习最优调节


研究了事件驱动公式下离散时间非线性系统的自学习最优调节。通过迭代过程的收敛性讨论，开发了基于事件的自适应批评算法。建立了当前非线性设备的输入状态稳定性（ISS）分析。然后，证明了合适的触发条件以保证受控系统的ISS。采用迭代双启发式动态规划（DHP）策略来实现事件驱动框架。仿真算例验证了所构建方法的适用性。与传统的DHP算法相比，基于偶数的算法能够大幅减少控制输入的更新次数，同时仍然保持令人印象深刻的性能。