Name
Title | ||
|---|---|---|
A Reinforcement Learning-Based Control Approach for Unknown Nonlinear Systems with Persistent Adversarial Inputs |
Abstract | ||
|---|---|---|
This paper develops an intelligent control method based on reinforcement learning techniques for unknown nonlinear continuous-time systems in an adversarial environment. The developed method can automatically learn the optimal control input for the system and also predict the worst case adversarial input that one adversary can bring into. Besides, we assume that the agent can only observe partial information of the environment during the learning process. Therefore, a neural network-based observer is developed to adaptively reconstruct the hidden states and dynamics. Then, theoretical analysis is provided to show the stability of the developed intelligent control and the accuracy of the established observer. This method has been applied on a torsional pendulum system and the results demonstrate the effectiveness of the designed approach. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9534429 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
Reinforcement learning, zero-sum games, neural networks, observer, online learning and control | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangnan Zhong | 1 | 346 | 16.35 |
| Haibo He | 2 | 3653 | 213.96 |