| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 6864428 | Neurocomputing | 2018 | 13 Pages |
Abstract
A feedback solution for approximate optimal scheduling of switched systems with autonomous subsystems and continuous-time dynamics is presented. The proposed solution is based on policy iteration algorithm which provides the optimal switching schedule. Algorithms for offline, online, and concurrent implementation of the proposed solution are presented. For online and concurrent training, gradient descent training laws are used and the performance of the training laws is analyzed. The effectiveness of the presented algorithms is verified through numerical simulations.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Tohid Sardarmehni, Ali Heydari,