LEADER 04381nam 22007695 450 001 9910254202103321 005 20221021220141.0 010 $a3-319-26995-X 024 7 $a10.1007/978-3-319-26995-5 035 $a(CKB)3710000000532202 035 $a(EBL)4178580 035 $a(SSID)ssj0001584680 035 $a(PQKBManifestationID)16264542 035 $a(PQKBTitleCode)TC0001584680 035 $a(PQKBWorkID)14864334 035 $a(PQKB)10012066 035 $a(DE-He213)978-3-319-26995-5 035 $a(MiAaPQ)EBC4178580 035 $a(PPN)190523336 035 $a(EXLCZ)993710000000532202 100 $a20151125d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMixed-integer representations in control design $emathematical foundations and applications /$fby Ionela Prodan, Florin Stoican, Sorin Olaru, Silviu-Iulian Niculescu 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (115 p.) 225 1 $aSpringerBriefs in Control, Automation and Robotics,$x2192-6786 300 $aDescription based upon print version of record. 311 $a3-319-26993-3 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aIntroduction -- Non-Covex Region Characterization by Hyperplane Arrangements -- Mixed-Integer Representations -- Examples of Multi-Agent Control Problems -- Conclusions. 330 $aIn this book, the authors propose efficient characterizations of the non-convex regions that appear in many control problems, such as those involving collision/obstacle avoidance and, in a broader sense, in the description of feasible sets for optimization-based control design involving contradictory objectives. The text deals with a large class of systems that require the solution of appropriate optimization problems over a feasible region, which is neither convex nor compact. The proposed approach uses the combinatorial notion of hyperplane arrangement, partitioning the space by a finite collection of hyperplanes, to describe non-convex regions efficiently. Mixed-integer programming techniques are then applied to propose acceptable formulations of the overall problem. Multiple constructions may arise from the same initial problem, and their complexity under various parameters - space dimension, number of binary variables, etc. - is also discussed. This book is a useful tool for academic researchers and graduate students interested in non-convex systems working in control engineering area, mobile robotics and/or optimal planning and decision-making. 410 0$aSpringerBriefs in Control, Automation and Robotics,$x2192-6786 606 $aControl engineering 606 $aSystem theory 606 $aCalculus of variations 606 $aRobotics 606 $aAutomation 606 $aControl and Systems Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/T19010 606 $aSystems Theory, Control$3https://scigraph.springernature.com/ontologies/product-market-codes/M13070 606 $aCalculus of Variations and Optimal Control; Optimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26016 606 $aRobotics and Automation$3https://scigraph.springernature.com/ontologies/product-market-codes/T19020 615 0$aControl engineering. 615 0$aSystem theory. 615 0$aCalculus of variations. 615 0$aRobotics. 615 0$aAutomation. 615 14$aControl and Systems Theory. 615 24$aSystems Theory, Control. 615 24$aCalculus of Variations and Optimal Control; Optimization. 615 24$aRobotics and Automation. 676 $a629.8312 700 $aProdan$b Ionela$4aut$4http://id.loc.gov/vocabulary/relators/aut$01061088 702 $aStoican$b Florin$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aOlaru$b Sorin$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aNiculescu$b Silviu-Iulian$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254202103321 996 $aMixed-Integer Representations in Control Design$92517418 997 $aUNINA