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         "type" : "Publication",
         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/2170332c3f0ced359dd0fcfb339ab061b/britsteiner",         
         "tags" : [
            "Kernel","anm","approximation,","control,","dynamic","feedback","greedy","ians","optimal","principle,","programming","techniques"
         ],
         
         "intraHash" : "170332c3f0ced359dd0fcfb339ab061b",
         "interHash" : "4bfa5b3c1d35696d5b7b3d202217e601",
         "label" : "Data-driven surrogates of value functions and applications to feedback control for dynamical systems",
         "user" : "britsteiner",
         "description" : "",
         "date" : "2021-09-29 14:33:27",
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         "pub-type": "article",
         "journal": "IFAC-PapersOnLine",
         "year": "2018", 
         "url": "http://www.sciencedirect.com/science/article/pii/S2405896318300570", 
         
         "author": [ 
            "A. Schmidt","Bernard Haasdonk"
         ],
         "authors": [
         	
            	{"first" : "A.",	"last" : "Schmidt"},
            	{"first" : "Bernard",	"last" : "Haasdonk"}
         ],
         "volume": "51","number": "2","pages": "307--312","note": "9th Vienna International Conference on Mathematical Modelling","abstract": "Dealing with high-dimensional feedback control problems is a difficult\n\ttask when the classical dynamic programming principle is applied.\n\tExisting techniques restrict the application to relatively low dimensions\n\tsince the discretizations typically suffer from the curse of dimensionality.\n\tIn this paper we introduce a novel approximation technique for the\n\tvalue function of an infinite horizon optimal control. The method\n\tis based on solving optimal open loop control problems on a finite\n\thorizon with a sampling of the global value function along the generated\n\ttrajectories. For the interpolation we choose a kernel orthogonal\n\tgreedy strategy, because these methods are able to produce extreme\n\tsparse surrogates and enable rapid evaluations in high dimensions.\n\tTwo numerical examples prove the performance of the approach and\n\tshow that the method is able to deal with high-dimensional feedback\n\tcontrol problems, where the dimensionality prevents the approximation\n\tby most existing methods.",
         "owner" : "santinge",
         
         "issn" : "2405-8963",
         
         "doi" : "https://doi.org/10.1016/j.ifacol.2018.03.053",
         
         "bibtexKey": "Schmidt2018f"

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