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dc.contributor.authorZhao, Kejunzh_CN
dc.contributor.authorLu, Xinzh_CN
dc.contributor.authorZheng, Wenzhouzh_CN
dc.contributor.authorHuang, Chunqingzh_CN
dc.contributor.author黄春庆zh_CN
dc.date.accessioned2015-07-22T02:39:50Z
dc.date.available2015-07-22T02:39:50Z
dc.date.issued2012zh_CN
dc.identifier.citationProceedings - 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012, 2012:2366-2370zh_CN
dc.identifier.other20130515971532zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/86803
dc.descriptionConference Name:2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012. Conference Address: Chongqing, China. Time:May 29, 2012 - May 31, 2012.zh_CN
dc.description.abstractAs for MPC controllers, hard-constraint such as constraints on input magnitude or/and rate are generally regarded as an inviolable rule that has to be satisfied strictly before the cost function is optimized at each sampling instant. Such strategy is to result in control conservatism of MPC, as well as infeasibility problem. In this paper, constraint-softening technique is proposed, in which hard-constraint are relaxed appropriately and hence the region of hard-constraint is enlarged directly in optimizer of MPC. As a result, transient performance of the resulting system is significantly improved. Meanwhile, the feasibility problem is solved via this approach. A simulation result demonstrates the effectiveness of the proposed constraint-softening technique. 漏 2012 IEEE.zh_CN
dc.language.isoen_USzh_CN
dc.publisherIEEE Computer Societyzh_CN
dc.source.urihttp://dx.doi.org/10.1109/FSKD.2012.6234019zh_CN
dc.subjectFuzzy systemszh_CN
dc.subjectModel predictive controlzh_CN
dc.titleDirect relaxation of hard-constraint in Model Predictive Controlzh_CN
dc.typeConferencezh_CN


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