LEADER 05419nam 2200685Ia 450 001 9910137968803321 005 20230725045327.0 010 $a1-5231-1556-4 010 $a3-527-63625-0 010 $a1-283-64418-5 010 $a3-527-63626-9 010 $a3-527-63624-2 035 $a(CKB)3280000000000386 035 $a(EBL)1033310 035 $a(OCoLC)757511876 035 $a(SSID)ssj0000622309 035 $a(PQKBManifestationID)11367296 035 $a(PQKBTitleCode)TC0000622309 035 $a(PQKBWorkID)10643514 035 $a(PQKB)10876266 035 $a(MiAaPQ)EBC1033310 035 $a(Au-PeEL)EBL1033310 035 $a(CaPaEBR)ebr10606039 035 $a(EXLCZ)993280000000000386 100 $a20110330d2011 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aPredictive control in process engineering$b[electronic resource] $efrom the basics to the applications /$fRobert Haber, Ruth Bars, and Ulrich Schmitz 210 $aWeinheim $cWiley-VCH$dc2011 215 $a1 online resource (632 p.) 300 $aDescription based upon print version of record. 311 $a3-527-31492-X 320 $aIncludes bibliographical references and index. 327 $aPredictive Control in Process Engineering; Contents; Preface; References; Notation and Abbreviations; 1 Introduction to Predictive Control; 1.1 Preview of Predictive Control; 1.1.1 Prediction of the Reference Value; 1.1.2 Prediction of the Disturbance; 1.2 Manipulated, Reference, and Controlled Signals; 1.3 Cost Function of Predictive Control; 1.4 Reference Signal and Disturbance Preview, Receding Horizon, One-Step-Ahead, and Long-Range Optimal Control; 1.5 Free and Forced Responses of the Predicted Controlled Variable; 1.6 Minimization of the Cost Function 327 $a1.6.1 Minimization Algorithms for Nonlinear Processes with or without Constraints 1.6.2 Minimization of the Quadratic Cost Function for Linear Processes without Constraints; 1.7 Simple Tuning Rules of Predictive Control; 1.8 Control of Different Linear SISO Processes; 1.9 Control of Different Linear MIMO Processes; 1.10 Control of Nonlinear Processes; 1.11 Control under Constraints; 1.12 Robustness; 1.13 Summary; References; 2 Linear SISO Model Descriptions; 2.1 Nonparametric System Description; 2.1.1 FIR Model; 2.1.2 FSR Model; 2.1.3 Relationship between the FIRs and the FSRs 327 $a2.1.4 Disturbance Model 2.2 Pulse-Transfer Function Model; 2.2.1 Pulse-Transfer Function and Difference Equation; 2.2.2 Relationship between the Pulse-Transfer Function, the Weighting Function, and the Step Response Models; 2.2.3 Disturbance Model; 2.3 Discrete-Time State Space Model; 2.3.1 Minimal-Order State Space Representation; 2.3.2 Non-Minimal-Order State Space Representations; 2.4 Summary; References; 3 Predictive Equations of Linear SISO Models; 3.1 Predictive Equations Based on Nonparametric Models; 3.1.1 Predictive Equations of the Impulse Response Model 327 $a3.1.2 Predictive Equations of the Step Response Model 3.2 Predictive Equations Based on the Pulse-Transfer Function; 3.2.1 Repeated Substitution of the Process Model Equation; 3.2.2 Prediction by Solving the Diophantine Equation; 3.2.3 Prediction if the Additive Noise Is Autoregressive; 3.2.4 Prediction in the Presence of a Measurable Disturbance; 3.2.5 Prediction if the Additive Noise Is Nonautoregressive; 3.2.6 Matrix Calculation Method; 3.3 Predictive Equations of the State Space Model; 3.4 Summary; References; 4 Predictive On-Off Control 327 $a4.1 Classical On-Off Control by Means of Relay Characteristics 4.2 Predictive Set Point Control; 4.2.1 Cost Function Minimization by a Selection Strategy; 4.2.2 Cost Function Minimization by a Genetic Algorithm; 4.2.3 Simulation and Comparison of the Predictive Set Point Control Algorithms; 4.3 Predictive Start-Up Control at a Reference Signal Change; 4.4 Predictive Gap Control; 4.4.1 Quadratic Cost Function Minimization by the Selection Strategy or the Genetic Algorithm; 4.4.2 Quasi Continuous-Time Optimization; 4.4.3 Minimizing a Limit-Violation-Time-Point-Dependent Cost Function 327 $a4.4.4 Online Start-Up Strategy 330 $aDescribing the principles and applications of single input, single output and multivariable predictive control in a simple and lively manner, this practical book discusses topics such as the handling of on-off control, nonlinearities and numerical problems. It gives guidelines and methods for reducing the computational demand for real-time applications. With its many examples and several case studies (incl. injection molding machine and waste water treatment) and industrial applications (stripping column, distillation column, furnace) this is invaluable reading for students and engineers who w 606 $aPredictive control 606 $aProduction engineering 615 0$aPredictive control. 615 0$aProduction engineering. 676 $a629.8 686 $aZQ 9910$2rvk 686 $a660$2sdnb 700 $aHaber$b Robert$f1948-$0893874 701 $aBars$b R$0893875 701 $aSchmitz$b Ulrich$cDipl.-Ing.$0599864 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910137968803321 996 $aPredictive control in process engineering$91996684 997 $aUNINA