Name
Abstract | ||
|---|---|---|
In this paper, a novel integral reinforcement learning (IRL)-based event-triggered adaptive dynamic programming scheme is developed for input-saturated continuous-time nonlinear systems. By using the IRL technique, the learning system does not require the knowledge of the drift dynamics. Then, a single critic neural network is designed to approximate the unknown value function and its learning is not subjected to the requirement of an initial admissible control. In order to reduce computational and communication costs, the event-triggered control law is designed. The triggering threshold is given to guarantee the asymptotic stability of the control system. Two examples are employed in the simulation studies, and the results verify the effectiveness of the developed IRL-based event-triggered control method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neunet.2020.07.016 | Neural Networks |
Keywords | DocType | Volume |
Adaptive dynamic programming,Integral reinforcement learning,Neural networks,Event-triggered control,Input saturation | Journal | 131 |
Issue | ISSN | Citations |
1 | 0893-6080 | 1 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shan Xue | 1 | 51 | 11.69 |
| Biao Luo | 2 | 554 | 23.80 |
| Derong Liu | 3 | 5457 | 286.88 |