Name
Title | ||
|---|---|---|
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 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. |
Year | Venue | Field |
|---|---|---|
2015 | 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | Dynamic programming,Control theory,Nonlinear system,Control theory,Computer science,Input/output,System dynamics,Observer (quantum physics),Facsimile,Computation |
DocType | ISSN | Citations |
Conference | 2161-4393 | 5 |
PageRank | References | Authors |
0.46 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangnan Zhong | 1 | 346 | 16.35 |
| Zhen Ni | 2 | 525 | 33.47 |
| Haibo He | 3 | 3653 | 213.96 |