Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4947626 | Neurocomputing | 2017 | 31 Pages |
Abstract
In this paper, we propose a neural-network (NN)-based online off-policy algorithm to optimize a class of nonlinear continuous-time time-delay systems during finite time horizon. The online off-policy algorithm is used to learn the two-stage solution to the time-varying Hamilton-Jacobi-Bellman (HJB) equation without requiring the knowledge of the time-delay system dynamics. The algorithm is implemented by using an actor-critic NN structure with time-varying activation functions. The weights of the two NNs are tuned simultaneously in real-time by considering both the residual error and the terminal error. Two simulation examples demonstrate the applicability of the proposed algorithm.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Xiaohong Cui, Huaguang Zhang, Yanhong Luo, He Jiang,