Predictive Control in Process Engineering (PDF)

Preface Notation and Abbreviations INTRODUCTION TO PREDICTIVE CONTROL Preview of Predictive Control Manipulated, Reference, and Controlled Signals Cost Function of Predictive Control Reference Signal and Disturbance Preview, Receding Horizon, One-Step-Ahead, and Long-Range Optimal Control Free and Forced Responses of the Predicted Controlled Variable Minimization of the Cost Function Simple Tuning Rules of Predictive Control Control of Different Linear SISO Processes Control of Different Linear MIMO Processes Control of Nonlinear Processes Control under Constraints Robustness Summary LINEAR SISO MODEL DESCRIPTIONS Nonparametric System Description Pulse-Transfer Function Model Discrete-Time State Space Model Summary PREDICTIVE ON-OFF CONTROL Classical On-Off Control by Means of Relay Characteristics Predictive Set Point Control Predictive Start-Up Control at a Reference Signal Change Predictive Gap Control Case Study: Temperature Control of an Electrical Heat Exchanger Summary GENERALIZED PREDICTIVE CONTROL OF LINEAR SISO PROCESSES Control Algorithm without Constraints Linear Polynomial Form of Unconstrained GPC Tuning the Controller Parameters Blocking and Coincidence Points Techniques Measured Disturbance Feed-Forward Compensation Control Algorithm with Constraints Extended GPC with Terminal Methods Summary PREDICTIVE PID CONTROL ALGORITHMS Predictive PI(D) Control Structure Predictive PI Control Algorithm Predictive PID Control Algorithm Equivalence between the Predictive PI(D) Algorithm and the Generalized Predictive Control Algorithm Tuning of Predictive PI(D) Algorithms Robustifying Effects Applied for Predictive PI(D) Control Algorithms Summary PREDICTIVE CONTROL OF MULTIVARIABLE PROCESSES Model Descriptions Predictive Equations The Control Algorithm Polynomial Form of the Controller (without Matrix Inversion) Pairing of the Controlled and the Manipulated Variables Scaling of the Controlled and the Manipulated Variables Tuning Decoupling Control Case Study:

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.

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.

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.

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