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To overcome the bottleneck of solving Hamilton\u2013Jacobi\u2013Bellman equations, several state\u2010of\u2010the\u2010art ADP approaches are reviewed in this paper. First, two model\u2010based 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\u2010time optimal control strategy is tested on a power system.<\/jats:p>","DOI":"10.1155\/2021\/5549678","type":"journal-article","created":{"date-parts":[[2021,4,17]],"date-time":"2021-04-17T20:35:07Z","timestamp":1618691707000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neural Network\u2010Based Intelligent Computing Algorithms for Discrete\u2010Time Optimal Control with the Application to a Cyberphysical Power 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