Event-triggered adaptive dynamic programming for continuous-time nonlinear system using measured input-output data
Document Type
Conference Proceeding
Date of Original Version
9-28-2015
Abstract
In this paper, we propose a novel event-triggered adaptive dynamic programming (ADP) method using only the input-output data. Event-triggered method is widely used for its computational efficiency capacity. Comparing with the traditional method which updates the controller periodically, the event-triggered method only updates the controller when it is necessary and therefore the computation is reduced. Generally, the triggered condition is based on the system current and sampled states. In this paper, we consider a neural-network-based observer to recover the system dynamics using the measured input-output data. The triggered instants are calculated according to the recovered state. Stability analysis of the proposed approach is presented. We verify our proposed method through a robot-arm example.
Publication Title, e.g., Journal
Proceedings of the International Joint Conference on Neural Networks
Volume
2015-September
Citation/Publisher Attribution
Zhong, Xiangnan, Zhen Ni, and Haibo He. "Event-triggered adaptive dynamic programming for continuous-time nonlinear system using measured input-output data." Proceedings of the International Joint Conference on Neural Networks 2015-September, (2015). doi: 10.1109/IJCNN.2015.7280471.