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Adaptive control is developed to deal with point-to-point movement, and learning control and neural networks control are developed to generate periodic and arbitrary continuous trajectories, respectively. Stability and tracking performance of the closed-loop system are discussed through rigorous analysis. The validity of the proposed method is verified through simulation and experiment studies.<\/jats:p>","DOI":"10.1017\/s0263574714002240","type":"journal-article","created":{"date-parts":[[2014,8,22]],"date-time":"2014-08-22T15:36:53Z","timestamp":1408721813000},"page":"1260-1281","source":"Crossref","is-referenced-by-count":42,"title":["Force tracking control for motion synchronization in human-robot collaboration"],"prefix":"10.1017","volume":"34","author":[{"given":"Yanan","family":"Li","sequence":"first","affiliation":[]},{"given":"Shuzhi Sam","family":"Ge","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2014,8,26]]},"reference":[{"key":"S0263574714002240_ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s004220050435"},{"key":"S0263574714002240_ref4","doi-asserted-by":"crossref","unstructured":"Y. Maeda , H. Takayuki and A. 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