LEADER 03875nam 2200613 a 450 001 9910437921303321 005 20200520144314.0 010 $a1-283-69697-5 010 $a1-4471-4351-5 024 7 $a10.1007/978-1-4471-4351-2 035 $a(CKB)2670000000277593 035 $a(EBL)994430 035 $a(OCoLC)813837898 035 $a(SSID)ssj0000767048 035 $a(PQKBManifestationID)11421291 035 $a(PQKBTitleCode)TC0000767048 035 $a(PQKBWorkID)10739612 035 $a(PQKB)11410718 035 $a(DE-He213)978-1-4471-4351-2 035 $a(MiAaPQ)EBC994430 035 $a(PPN)168293269 035 $a(EXLCZ)992670000000277593 100 $a20121008d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aHybrid predictive control for dynamic transport problems /$fAlfredo A. Nunez, Doris A. Saez, Cristian E. Cortes 205 $a1st ed. 2013. 210 $aLondon $cSpringer$d2013 215 $a1 online resource (182 p.) 225 0$aAdvances in industrial control,$x1430-9491 300 $aDescription based upon print version of record. 311 $a1-4471-5940-3 311 $a1-4471-4350-7 320 $aIncludes bibliographical references and index. 327 $aHybrid Predictive Control: Mono-objective and Multi-objective Design -- Hybrid Predictive Control for a Dial-a-ride System -- Hybrid Predictive Control for Operational Decisions in Public Transport Systems. 330 $aHybrid Predictive Control for Dynamic Transport Problems develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from the area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed-integer optimization problems dynamically, is readily extendable to other industrial processes. The main topics of this book are: ?hybrid predictive control (HPC) design based on evolutionary multiobjective optimization (EMO); ?HPC based on EMO for dial-a-ride systems; and ?HPC based on EMO for operational decisions in public transport systems. Hybrid Predictive Control for Dynamic Transport Problems is a comprehensive analysis of HPC and its application to dynamic transport systems. Introductory material on evolutionary algorithms is presented in summary in an appendix. The text will be of interest to control and transport engineers working on the operational optimization of transport systems and to academic researchers working with hybrid systems. The potential applications of the generic methods presented here in other process fields will appeal to a wider group of researchers, scientists and graduate students working in other control-related disciplines. 410 0$aAdvances in Industrial Control,$x1430-9491 606 $aControl theory 615 0$aControl theory. 676 $a629.8 676 $a629.836 700 $aNunez$b Alfredo A$01750355 701 $aSaez$b Doris A$01750356 701 $aCortes$b Cristian E$01750357 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437921303321 996 $aHybrid predictive control for dynamic transport problems$94184981 997 $aUNINA