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Preface<br> Notation and Abbreviations<br> <br> INTRODUCTION TO PREDICTIVE CONTROL<br> Preview of Predictive Control<br> Manipulated, Reference, and Controlled Signals<br> Cost Function of Predictive Control<br> Reference Signal and Disturbance Preview, Receding Horizon, One-Step-Ahead, and Long-Range Optimal Control<br> Free and Forced Responses of the Predicted Controlled Variable<br> Minimization of the Cost Function<br> Simple Tuning Rules of Predictive Control<br> Control of Different Linear SISO Processes<br> Control of Different Linear MIMO Processes<br> Control of Nonlinear Processes<br> Control under Constraints<br> Robustness<br> Summary<br> <br> LINEAR SISO MODEL DESCRIPTIONS<br> Nonparametric System Description<br> Pulse-Transfer Function Model<br> Discrete-Time State Space Model<br> Summary<br> <br> PREDICTIVE ON-OFF CONTROL<br> Classical On-Off Control by Means of Relay Characteristics<br> Predictive Set Point Control<br> Predictive Start-Up Control at a Reference Signal Change<br> Predictive Gap Control<br> Case Study: Temperature Control of an Electrical Heat Exchanger<br> Summary<br> <br> GENERALIZED PREDICTIVE CONTROL OF LINEAR SISO PROCESSES<br> Control Algorithm without Constraints<br> Linear Polynomial Form of Unconstrained GPC<br> Tuning the Controller Parameters<br> Blocking and Coincidence Points Techniques<br> Measured Disturbance Feed-Forward Compensation<br> Control Algorithm with Constraints<br> Extended GPC with Terminal Methods<br> Summary<br> <br> PREDICTIVE PID CONTROL ALGORITHMS<br> Predictive PI(D) Control Structure<br> Predictive PI Control Algorithm<br> Predictive PID Control Algorithm<br> Equivalence between the Predictive PI(D) Algorithm and the Generalized Predictive Control Algorithm<br> Tuning of Predictive PI(D) Algorithms<br> Robustifying Effects Applied for Predictive PI(D) Control Algorithms<br> Summary<br> <br> PREDICTIVE CONTROL OF MULTIVARIABLE PROCESSES<br> Model Descriptions<br> Predictive Equations<br> The Control Algorithm<br> Polynomial Form of the Controller (without Matrix Inversion)<br> Pairing of the Controlled and the Manipulated Variables<br> Scaling of the Controlled and the Manipulated Variables<br> Tuning<br> Decoupling Control<br> Case Study: Control of a Distillation Column<br> Summary<br> <br> ESTIMATION OF THE PREDICTIVE EQUATIONS<br> LS Parameter Estimation<br> More-Steps-Ahead Prediction Based on the Estimated Process Model<br> Long-Range Optimal Single-Process Model Identification<br> Multi-Step-Ahead Predictive Equation Identification<br> Comparison of the Long-Range Optimal Identification Algorithms<br> Case Study: Level Control in a Two-Tank Plant<br> Summary<br> <br> MULTIMODEL AND MULTICONTROLLER APPROACHES<br> Nonlinear Process Models<br> Predictive Equations<br> The Control Algorithm<br> Case Study<br> Summary<br> <br> GPC OF NONLINEAR SISO PROCESSES<br> Nonlinear Process Models<br> Predictive Equations for the Nonparametric and Parametric Hammerstein and Volterra Models<br> Control Based on Nonparametric and Parametric Hammerstein and Volterra Models<br> Control Based on Linearized Models<br> Control Based on Nonlinear Free and Linearized Forced Responses<br> Case Study: Level Control of a Two-Tank Plant<br> Summary<br> <br> PREDICTIVE FUNCTIONAL CONTROL<br> Control Strategy and Controller Parameters for a Constant Set Point<br> PFC for Aperiodic Processes<br> PFC with Disturbance Feed-Forward<br> PFC with Constraints<br> Nonlinear PFC for Processes with Signal-Dependent Parameters<br> Case Study: Temperature Control of a Hot Air Blower<br> Summary<br> <br> CASE STUDIES<br> Predictive Temperature Control of an Injection Molding Machine<br> Wastewater Quality Control of an Intermittently Operated Plant<br> Wastewater Quality Control with Pre-Denitrification<br> <br> INDUSTRIAL APPLICATIONS<br> Concentration Control and Pressure Minimization of a Petrochemical Distillation Column<br> Concentration Control and Reducing Steam Consumption in a Stripping Column<br> Temperature and Combustion Control of a Gas-Heated Furnace for Chemical Gasoline<br> <br> PRACTICAL ASPECTS AND FUTURE TRENDS<br> Classification of a Predictive Control Project<br> Project Implementation<br> Implementation of a Predictive Controller<br> Future Trends<br> Summary<br> <br>

Robert Haber had studied electrical engineering at the Budapest University of Technology, where he also has received the Ph.D. in control theory. He is currently head of the Laboratory for Process Automation in the Faculty of Process Engineering, Energy and Mechanical Systems at the Cologne University of Applied Sciences. His research interests include process automation, experimental identification, model based control and intelligent process data analysis.<br> <br> Ruth Bars graduated at the Electrical Engineering Faculty of the Budapest University of Technology, Hungary, where she has gained also her Ph.D. degree. Currently she is associate professor at the Department of Automation and Applied Informatics at the Budapest University of Technology and Economics. Her research interests are in predictive control and in developing new ways of control education. He was involved in IFAC International Federation of Automatic Control as Technical and Coordinating Committee chair.<br> <br> Ulrich Schmitz studied chemical engineering and plant design at the Cologne University of Applied Sciences. Prior to this he was working as an operator in a petrochemical plant. From 2001 till 2005 he was a scientific assistant at the Cologne University of Applied Sciences and took part in a cooperative doctoral project between the Universities in Cologne and Budapest. In 2007 he received his Ph.D. from the Budapest University of Technology and Economics. Since 2005 he has been working as an APC technologist for Shell Deutschland Oil at the Rhineland Refinery in Germany.<br>

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, 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 aim to understand and apply predictive control in a wide variety of process engineering application areas.<br> <br> From the contents:<br> * Predictive on-off control<br> * Predictive control of linear single-input<br> * Single-output and multivariable processes<br> * Nonlinear predictive control<br> * Predictive PI(D) control<br> * Case Studies<br> * Industrial Applications<br> * Practical Aspects and some future trends<br> <br> <br>

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