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The key feature in this strategy is to solve the IOC problem repeatedly for each receding horizon of the model predictive control approach. From another perspective, MPC structure is inserted to IOC problem and thus, IOC problem is solved repeatedly using different initial conditions at the beginning of each receding horizon. In the solution phase of IOC, the parameters of the candidate control Lyapunov function matrix are estimated using the global evolutionary Big Bang-Big Crunch (BB-BC) optimization algorithm in an on-line manner. Thus, the proposed control structure solves the optimal control problem in classical MPC approach to the search of an appropriate candidate control Lyapunov function matrix for each control horizon. The comparison of the proposed method with the other related control methods are performed on the ball and beam system via simulations and real-time applications. <\/jats:p>","DOI":"10.1177\/0142331219884803","type":"journal-article","created":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T08:49:25Z","timestamp":1574671765000},"page":"1122-1134","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Fusion of inverse optimal and model predictive control strategies"],"prefix":"10.1177","volume":"42","author":[{"given":"L\u00fctfi","family":"Ulusoy","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Nam\u0131k Kemal University, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4150-4278","authenticated-orcid":false,"given":"M\u00fcjde","family":"G\u00fczelkaya","sequence":"additional","affiliation":[{"name":"Department of Control and Automation Engineering, 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