LEADER 04142nam 2200613 450 001 9910674355703321 005 20240129144845.0 010 $a3-031-21139-1 024 7 $a10.1007/978-3-031-21139-3 035 $a(MiAaPQ)EBC7205355 035 $a(Au-PeEL)EBL7205355 035 $a(CKB)26170315500041 035 $a(DE-He213)978-3-031-21139-3 035 $a(PPN)268204977 035 $a(EXLCZ)9926170315500041 100 $a20230520d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aAn Introduction to Optimal Control Theory $eThe Dynamic Programming Approach /$fOne?simo Herna?ndez-Lerma [and three others] 205 $a1st ed. 2023. 210 1$aCham, Switzerland :$cSpringer, Springer Nature Switzerland AG,$d[2023] 210 4$d©2023 215 $a1 online resource (279 pages) 225 1 $aTexts in Applied Mathematics Series ;$vVolume 76 311 08$aPrint version: Hernández-Lerma, Onésimo An Introduction to Optimal Control Theory Cham : Springer International Publishing AG,c2023 9783031211386 320 $aIncludes bibliographical references (pages 263-270) and index. 327 $aIntroduction: optimal control problems-. Discrete-time deterministic systems -- Discrete-time stochastic control systems -- Continuous-time deterministic systems -- Continuous-time Markov control processes -- Controlled diffusion processes -- Appendices -- Bibliography -- Index. 330 $aThis book introduces optimal control problems for large families of deterministic and stochastic systems with discrete or continuous time parameter. These families include most of the systems studied in many disciplines, including Economics, Engineering, Operations Research, and Management Science, among many others. The main objective is to give a concise, systematic, and reasonably self contained presentation of some key topics in optimal control theory. To this end, most of the analyses are based on the dynamic programming (DP) technique. This technique is applicable to almost all control problems that appear in theory and applications. They include, for instance, finite and infinite horizon control problems in which the underlying dynamic system follows either a deterministic or stochastic difference or differential equation. In the infinite horizon case, it also uses DP to study undiscounted problems, such as the ergodic or long-run average cost. After a general introduction to control problems, the book covers the topic dividing into four parts with different dynamical systems: control of discrete-time deterministic systems, discrete-time stochastic systems, ordinary differential equations, and finally a general continuous-time MCP with applications for stochastic differential equations. The first and second part should be accessible to undergraduate students with some knowledge of elementary calculus, linear algebra, and some concepts from probability theory (random variables, expectations, and so forth). Whereas the third and fourth part would be appropriate for advanced undergraduates or graduate students who have a working knowledge of mathematical analysis (derivatives, integrals, ...) and stochastic processes. 410 0$aTexts in applied mathematics ;$vVolume 76. 606 $aControl theory 606 $aMathematical optimization 606 $aStochastic processes 606 $aTeoria de control$2thub 606 $aOptimització matemàtica$2thub 606 $aProcessos estocàstics$2thub 608 $aLlibres electrònics$2thub 615 0$aControl theory. 615 0$aMathematical optimization. 615 0$aStochastic processes. 615 7$aTeoria de control 615 7$aOptimització matemàtica 615 7$aProcessos estocàstics 676 $a515.642 700 $aHerna?ndez-Lerma$b O$g(One?simo),$0350131 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910674355703321 996 $aAn Introduction to Optimal Control Theory$93374123 997 $aUNINA