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By utilizing the Newton\u2013Euler iteration technique, the robotic system\u2019s dynamic model is formulated and the state space equation is derived. According to adaptive dynamic programming (ADP) and neural network algorithm, the trajectory tracking control problem is transformed into a zero-sum game-based optimal control issue. The optimal control policy and worst disturbance policy are obtained by Hamilton\u2013Jacobi\u2013Issacs (HJI) function, respectively. Unlike the conventional learning\u2013based robotic control method, the proposed zero-sum game-based method no need extra sub-controller that can reduce the computational burden. The reconfigurable robot system\u2019s tracking error is uniformly ultimately bounded by the Lyapunov theorem. Finally, simulation experiments demonstrate the advantages of the proposed method. <\/jats:p>","DOI":"10.1177\/01423312221109726","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T08:21:51Z","timestamp":1662020511000},"page":"466-475","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Decentralized position\u2013force zero-sum approximate optimal control for reconfigurable robots with unmodeled dynamic"],"prefix":"10.1177","volume":"45","author":[{"given":"Xinye","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Mechanical and Control Engineering, Baicheng Normal University, China"},{"name":"Department of Control Science and Engineering, Changchun University of Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4292-6691","authenticated-orcid":false,"given":"Tianjiao","family":"An","sequence":"additional","affiliation":[{"name":"Department of 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