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Event-triggered adaptive dynamic programming for continuous-time nonlinear system using measured input-output data | IEEE Conference Publication | IEEE Xplore

Event-triggered adaptive dynamic programming for continuous-time nonlinear system using measured input-output data


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 it...Show More

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.
Date of Conference: 12-17 July 2015
Date Added to IEEE Xplore: 01 October 2015
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Conference Location: Killarney, Ireland

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