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It allows one to simultaneously obtain an optimal feedforward input and tuning parameter for a plant system, which minimizes a given cost function. In the proposed method, a virtual constraint by a potential energy prevents a biped robot from falling. The strength of the constraint is automatically mitigated by the IFT part of the proposed method, according to the progress of trajectory learning by the ILC part.<\/jats:p>","DOI":"10.1017\/s0263574712000756","type":"journal-article","created":{"date-parts":[[2013,1,23]],"date-time":"2013-01-23T08:13:40Z","timestamp":1358928820000},"page":"717-732","source":"Crossref","is-referenced-by-count":9,"title":["Gait generation via unified learning optimal control of Hamiltonian systems"],"prefix":"10.1017","volume":"31","author":[{"given":"Satoshi","family":"Satoh","sequence":"first","affiliation":[]},{"given":"Kenji","family":"Fujimoto","sequence":"additional","affiliation":[]},{"given":"Sang-Ho","family":"Hyon","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2013,1,23]]},"reference":[{"key":"S0263574712000756_ref37","unstructured":"S. Satoh and K. 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