Indexed by：
Abstract：
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.
Keyword：
Reprint Author's Address：
Source ：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
ISSN： 0018-9286
Year： 2020
Issue： 3
Volume： 65
Page： 1272-1279
6 . 8 0 0
JCR@2022
ESI Discipline： ENGINEERING;
ESI HC Threshold：115
Cited Count：
WoS CC Cited Count： 159
SCOPUS Cited Count： 177
ESI Highly Cited Papers on the List： 32 Unfold All
WanFang Cited Count：
Chinese Cited Count：
30 Days PV： 5
Affiliated Colleges：
