Data-Driven Robust Control Using Reinforcement Learning
This paper proposes a robust control design method using reinforcement learning for controlling partially-unknown dynamical systems under uncertain conditions. The method extends the optimal reinforcement learning algorithm with a new learning technique based on the robust control theory. By learnin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/4/2262 |
| Summary: | This paper proposes a robust control design method using reinforcement learning for controlling partially-unknown dynamical systems under uncertain conditions. The method extends the optimal reinforcement learning algorithm with a new learning technique based on the robust control theory. By learning from the data, the algorithm proposes actions that guarantee the stability of the closed-loop system within the uncertainties estimated also from the data. Control policies are calculated by solving a set of linear matrix inequalities. The controller was evaluated using simulations on a blood glucose model for patients with Type 1 diabetes. Simulation results show that the proposed methodology is capable of safely regulating the blood glucose within a healthy level under the influence of measurement and process noises. The controller has also significantly reduced the post-meal fluctuation of the blood glucose. A comparison between the proposed algorithm and the existing optimal reinforcement learning algorithm shows the improved robustness of the closed-loop system using our method. |
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| ISSN: | 2076-3417 |