LEADER 05376nam 2200673 450 001 9910139015503321 005 20200903223051.0 010 $a1-118-79651-9 010 $a1-118-79635-7 010 $a1-118-79648-9 035 $a(CKB)2550000001115801 035 $a(EBL)1376955 035 $a(OCoLC)861529063 035 $a(SSID)ssj0001036150 035 $a(PQKBManifestationID)11992646 035 $a(PQKBTitleCode)TC0001036150 035 $a(PQKBWorkID)11050820 035 $a(PQKB)10406502 035 $a(MiAaPQ)EBC1376955 035 $a(Au-PeEL)EBL1376955 035 $a(CaPaEBR)ebr10756812 035 $a(CaONFJC)MIL516136 035 $a(PPN)190065273 035 $a(EXLCZ)992550000001115801 100 $a20130612d2013 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMetaheuristic optimization for the design of automatic control laws /$fGuillaume Sandou 210 1$aHoboken, NJ :$cISTE Ltd/John Wiley and Sons Inc,$d2013. 215 $a1 online resource (140 p.) 225 1 $aFocus automation and control series,$x2051-2481 300 $aDescription based upon print version of record. 311 $a1-84821-590-8 311 $a1-299-84885-0 320 $aIncludes bibliographical references and index. 327 $a""Cover ""; ""Title Page ""; ""Contents ""; ""Preface ""; ""Chapter 1. Introduction And Motivations ""; ""1.1. Introduction: automatic control and optimization ""; ""1.2. Motivations to use metaheuristic algorithms ""; ""1.3. Organization of the book ""; ""Chapter 2. Symbolic Regression "" 327 $a""2.1. Identification problematic and brief state of the art """"2.2. Problem statement and modeling ""; ""2.2.1. Problem statement ""; ""2.2.2. Problem modeling ""; ""2.3. Ant colony optimization ""; ""2.3.1. Ant colony social behavior ""; ""2.3.2. Ant colony optimization "" 327 $a""2.3.3. Ant colony for the identification of nonlinear functions with unknown structure """"2.4. Numerical results ""; ""2.4.1. Parameter settings ""; ""2.4.2. Experimental results ""; ""2.5. Discussion ""; ""2.5.1. Considering real variables ""; ""2.5.2. Local minima "" 327 $a""2.5.3. Identification of nonlinear dynamical systems """"2.6. A note on genetic algorithms for symbolic regression ""; ""2.7. Conclusions ""; ""Chapter 3. Pid Design Using Particle Swarm Optimization ""; ""3.1. Introduction ""; ""3.2. Controller tuning: a hard optimization problem "" 327 $a""3.2.1. Problem framework """"3.2.2. Expressions of time domain specifications ""; ""3.2.3. Expressions of frequency domain specifications ""; ""3.2.4. Analysis of the optimization problem ""; ""3.3. Particle swarm optimization implementation ""; ""3.4. PID tuning optimization "" 327 $a""3.4.1. Case study: magnetic levitation "" 330 $aThe classic approach in Automatic Control relies on the use of simplified models of the systems and reformulations of the specifications. In this framework, the control law can be computed using deterministic algorithms. However, this approach fails when the system is too complex for its model to be sufficiently simplified, when the designer has many constraints to take into account, or when the goal is not only to design a control but also to optimize it. This book presents a new trend in Automatic Control with the use of metaheuristic algorithms. These kinds of algorithm can optimize any cr 410 0$aFocus series in automation & control. 606 $aMathematical optimization 606 $aHeuristic algorithms 615 0$aMathematical optimization. 615 0$aHeuristic algorithms. 676 $a519.6 700 $aSandou$b Guillaume$0977732 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139015503321 996 $aMetaheuristic optimization for the design of automatic control laws$92227458 997 $aUNINA