Name
Title | ||
|---|---|---|
Event-Triggered Optimal Control With Performance Guarantees Using Adaptive Dynamic Programming. |
Abstract | ||
|---|---|---|
This paper studies the problem of event-triggered optimal control (ETOC) for continuous-time nonlinear systems and proposes a novel event-triggering condition that enables designing ETOC methods directly based on the solution of the Hamilton–Jacobi–Bellman (HJB) equation. We provide formal performance guarantees by proving a predetermined upper bound. Moreover, we also prove the existence of a lower bound for interexecution time. For implementation purposes, an adaptive dynamic programming (ADP) method is developed to realize the ETOC using a critic neural network (NN) to approximate the value function of the HJB equation. Subsequently, we prove that semiglobal uniform ultimate boundedness can be guaranteed for states and NN weight errors with the ADP-based ETOC. Simulation results demonstrate the effectiveness of the developed ADP-based ETOC method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TNNLS.2019.2899594 | IEEE transactions on neural networks and learning systems |
Keywords | Field | DocType |
Optimal control,Mathematical model,Artificial neural networks,Performance analysis,Dynamic programming,Indexes,Learning systems | Hamilton–Jacobi–Bellman equation,Dynamic programming,Nonlinear system,Optimal control,Upper and lower bounds,Computer science,Control theory,Bellman equation,Event triggered,Artificial intelligence,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
31 | 1 | 2162-237X |
Citations | PageRank | References |
17 | 0.53 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Biao Luo | 1 | 554 | 23.80 |
| Yin Yang | 2 | 1003 | 52.10 |
| Derong Liu | 3 | 5457 | 286.88 |
| Huai-Ning Wu | 4 | 2104 | 98.52 |