Neural Network-Based Intelligent Computing Algorithms for Discrete-Time Optimal Control with the Application to a Cyberphysical Power System
Adaptive dynamic programming (ADP), which belongs to the field of computational intelligence, is a powerful tool to address optimal control problems. To overcome the bottleneck of solving Hamilton–Jacobi–Bellman equations, several state-of-the-art ADP approaches are reviewed in this paper. First, tw...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5549678 |
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| _version_ | 1849696052055113728 |
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| author | Feng Jiang Kai Zhang Jinjing Hu Shunjiang Wang |
| author_facet | Feng Jiang Kai Zhang Jinjing Hu Shunjiang Wang |
| author_sort | Feng Jiang |
| collection | DOAJ |
| description | Adaptive dynamic programming (ADP), which belongs to the field of computational intelligence, is a powerful tool to address optimal control problems. To overcome the bottleneck of solving Hamilton–Jacobi–Bellman equations, several state-of-the-art ADP approaches are reviewed in this paper. First, two model-based offline iterative ADP methods including policy iteration (PI) and value iteration (VI) are given, and their respective advantages and shortcomings are discussed in detail. Second, the multistep heuristic dynamic programming (HDP) method is introduced, which avoids the requirement of initial admissible control and achieves fast convergence. This method successfully utilizes the advantages of PI and VI and overcomes their drawbacks at the same time. Finally, the discrete-time optimal control strategy is tested on a power system. |
| format | Article |
| id | doaj-art-cd2c50dce063479080230be7b5d5c6ef |
| institution | DOAJ |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-cd2c50dce063479080230be7b5d5c6ef2025-08-20T03:19:34ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55496785549678Neural Network-Based Intelligent Computing Algorithms for Discrete-Time Optimal Control with the Application to a Cyberphysical Power SystemFeng Jiang0Kai Zhang1Jinjing Hu2Shunjiang Wang3State Grid Liaoning Electric Power Company Limited, Shenyang 110006, ChinaState Grid Liaoning Electric Power Company Limited, Shenyang 110006, ChinaState Grid Liaoning Electric Power Company Limited, Shenyang 110006, ChinaState Grid Liaoning Electric Power Company Limited, Shenyang 110006, ChinaAdaptive dynamic programming (ADP), which belongs to the field of computational intelligence, is a powerful tool to address optimal control problems. To overcome the bottleneck of solving Hamilton–Jacobi–Bellman equations, several state-of-the-art ADP approaches are reviewed in this paper. First, two model-based offline iterative ADP methods including policy iteration (PI) and value iteration (VI) are given, and their respective advantages and shortcomings are discussed in detail. Second, the multistep heuristic dynamic programming (HDP) method is introduced, which avoids the requirement of initial admissible control and achieves fast convergence. This method successfully utilizes the advantages of PI and VI and overcomes their drawbacks at the same time. Finally, the discrete-time optimal control strategy is tested on a power system.http://dx.doi.org/10.1155/2021/5549678 |
| spellingShingle | Feng Jiang Kai Zhang Jinjing Hu Shunjiang Wang Neural Network-Based Intelligent Computing Algorithms for Discrete-Time Optimal Control with the Application to a Cyberphysical Power System Complexity |
| title | Neural Network-Based Intelligent Computing Algorithms for Discrete-Time Optimal Control with the Application to a Cyberphysical Power System |
| title_full | Neural Network-Based Intelligent Computing Algorithms for Discrete-Time Optimal Control with the Application to a Cyberphysical Power System |
| title_fullStr | Neural Network-Based Intelligent Computing Algorithms for Discrete-Time Optimal Control with the Application to a Cyberphysical Power System |
| title_full_unstemmed | Neural Network-Based Intelligent Computing Algorithms for Discrete-Time Optimal Control with the Application to a Cyberphysical Power System |
| title_short | Neural Network-Based Intelligent Computing Algorithms for Discrete-Time Optimal Control with the Application to a Cyberphysical Power System |
| title_sort | neural network based intelligent computing algorithms for discrete time optimal control with the application to a cyberphysical power system |
| url | http://dx.doi.org/10.1155/2021/5549678 |
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