Data-Driven Adaptive Dynamic Programming for Optimal Control of Continuous-Time Multicontroller Systems With Unknown Dynamics
This paper investigates the optimal control of continuous-time multi-controller systems with completely unknown dynamics using data-driven adaptive dynamic programming (DD-ADP). In this investigation, all controllers take actions together as a team, and they have precisely the same cost function, wh...
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| Published in: | IEEE access Vol. 10; pp. 41503 - 41511 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Piscataway
IEEE
2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 2169-3536, 2169-3536 |
| Online Access: | Get full text |
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| Summary: | This paper investigates the optimal control of continuous-time multi-controller systems with completely unknown dynamics using data-driven adaptive dynamic programming (DD-ADP). In this investigation, all controllers take actions together as a team, and they have precisely the same cost function, which is actually a fully cooperative game. According to optimal control theory, the HJB equation corresponding to the fully cooperative game is derived. To obtain the solution to HJB equation, a model-based policy iteration (PI) algorithm is first presented. On the basis of the PI algorithm, a DD-ADP algorithm without requiring the system dynamics is developed, and the neural networks (NNs) implementation scheme of the developed DD-ADP algorithm is given. Stability and convergence analysis are derived by Lyapunov theory. Finally, numerical simulation examples on linear and nonlinear multi-controller systems demonstrate the effectiveness of the designed scheme. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2169-3536 2169-3536 |
| DOI: | 10.1109/ACCESS.2022.3168032 |