Journal
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
Volume 65, Issue 3, Pages 1272-1279Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2019.2926167
Keywords
Event-driven formulation; iterative adaptive critic; neural networks; optimal regulation; self-learning control
Funding
- National Natural Science Foundation of China [61773373, 61890930-5, U1501251, 61533017]
- National Key Research and Development Project [2018YFC1900800-5]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available