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Statistical learning control of uncertain systems: theory and algorithms
Authors:V Koltchinskii  C T Abdallah  M Ariola and P Dorato
Institution:

a Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM 87131, USA

b Department of EECE, University of New Mexico, Albuquerque, NM 87131, USA

c Dipartimento di Informatica e Sistemistica, Università degli Studi di Napoli Federico II, Napoli, Italy

Abstract:It has recently become clear that many control problems are too difficult to admit analytic solutions. New results have also emerged to show that the computational complexity of some “solved” control problems is prohibitive. Many of these control problems can be reduced to decidability problems or to optimization questions. Even though such questions may be too difficult to answer analytically, or may not be answered exactly given a reasonable amount of computational resources, researchers have shown that we can “approximately” answer these questions “most of the time”, and have “high confidence” in the correctness of the answers.
Keywords:Empirical processes  Statistical learning  Robust control  Optimization
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