Ulteriori informazioni
Describing the principles and applications of single-input, single output and multivariable predictive control in a simple and lively manner, this practical book considers also such issues as the handling of on-off control, non-linearities 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 would aim to understand and apply predictive control in a wide variety of process engineering application areas.
From the contents:
Predictive on-off control
Predictive control of linear single-input
Single-output and multivariable processes
Nonlinear predictive control
Predictive PI(D) control
Case Studies
Industrial Applications
Practical Aspects and some future trends
Sommario
PrefaceNotation and AbbreviationsINTRODUCTION TO PREDICTIVE CONTROLPreview of Predictive ControlManipulated, Reference, and Controlled SignalsCost Function of Predictive ControlReference Signal and Disturbance Preview, Receding Horizon, One-Step-Ahead, and Long-Range Optimal ControlFree and Forced Responses of the Predicted Controlled VariableMinimization of the Cost FunctionSimple Tuning Rules of Predictive ControlControl of Different Linear SISO ProcessesControl of Different Linear MIMO ProcessesControl of Nonlinear ProcessesControl under ConstraintsRobustnessSummaryLINEAR SISO MODEL DESCRIPTIONSNonparametric System DescriptionPulse-Transfer Function ModelDiscrete-Time State Space ModelSummaryPREDICTIVE ON-OFF CONTROLClassical On-Off Control by Means of Relay CharacteristicsPredictive Set Point ControlPredictive Start-Up Control at a Reference Signal ChangePredictive Gap ControlCase Study: Temperature Control of an Electrical Heat ExchangerSummaryGENERALIZED PREDICTIVE CONTROL OF LINEAR SISO PROCESSESControl Algorithm without ConstraintsLinear Polynomial Form of Unconstrained GPCTuning the Controller ParametersBlocking and Coincidence Points TechniquesMeasured Disturbance Feed-Forward CompensationControl Algorithm with ConstraintsExtended GPC with Terminal MethodsSummaryPREDICTIVE PID CONTROL ALGORITHMSPredictive PI(D) Control StructurePredictive PI Control AlgorithmPredictive PID Control AlgorithmEquivalence between the Predictive PI(D) Algorithm and the Generalized Predictive Control AlgorithmTuning of Predictive PI(D) AlgorithmsRobustifying Effects Applied for Predictive PI(D) Control AlgorithmsSummaryPREDICTIVE CONTROL OF MULTIVARIABLE PROCESSESModel DescriptionsPredictive EquationsThe Control AlgorithmPolynomial Form of the Controller (without Matrix Inversion)Pairing of the Controlled and the Manipulated VariablesScaling of the Controlled and the Manipulated VariablesTuningDecoupling ControlCase Study: Control of a Distillation ColumnSummaryESTIMATION OF THE PREDICTIVE EQUATIONSLS Parameter EstimationMore-Steps-Ahead Prediction Based on the Estimated Process ModelLong-Range Optimal Single-Process Model IdentificationMulti-Step-Ahead Predictive Equation IdentificationComparison of the Long-Range Optimal Identification AlgorithmsCase Study: Level Control in a Two-Tank PlantSummaryMULTIMODEL AND MULTICONTROLLER APPROACHESNonlinear Process ModelsPredictive EquationsThe Control AlgorithmCase StudySummaryGPC OF NONLINEAR SISO PROCESSESNonlinear Process ModelsPredictive Equations for the Nonparametric and Parametric Hammerstein and Volterra ModelsControl Based on Nonparametric and Parametric Hammerstein and Volterra ModelsControl Based on Linearized ModelsControl Based on Nonlinear Free and Linearized Forced ResponsesCase Study: Level Control of a Two-Tank PlantSummaryPREDICTIVE FUNCTIONAL CONTROLControl Strategy and Controller Parameters for a Constant Set PointPFC for Aperiodic ProcessesPFC with Disturbance Feed-ForwardPFC with ConstraintsNonlinear PFC for Processes with Signal-Dependent ParametersCase Study: Temperature Control of a Hot Air BlowerSummaryCASE STUDIESPredictive Temperature Control of an Injection Molding MachineWastewater Quality Control of an Intermittently Operated PlantWastewater Quality Control with Pre-DenitrificationINDUSTRIAL APPLICATIONSConcentration Control and Pressure Minimization of a Petrochemical Distillation ColumnConcentration Control and Reducing Steam Consumption in a Stripping ColumnTemperature and Combustion Control of a Gas-Heated Furnace for Chemical GasolinePRACTICAL ASPECTS AND FUTURE TRENDSClassification of a Predictive Control ProjectProject ImplementationImplementation of a Predictive ControllerFuture TrendsSummary
Info autore
Prof. Robert Haber studied electrical and mechanical engineering at the universities of Budapest and Stuttgart. He gained his doctorate from the Technical University of Budapest in 1976. Since 1988 he has been professor for process engineering at the University of Applied Science in Cologne.
Ulrich Schmitz ist Professor für Germanistik/Linguistik und Sprachdidaktik an der Universität Duisburg-Essen. Er ist Leiter der International Linguistic Agency LAUD, des Linguistik-Servers LINSE und des Projekts E-Learning-Portal PortaLingua. Zahlreiche Publikationen zu Medienlinguistik, Sprache in alten und neuen Medien und Text-Bild-Beziehungen.
Riassunto
Describing 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 would wish to understand and apply predictive control in a wide variety of process engineering application areas.
