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" Methods of Model Based Process Control "
edited by Ridvan Berber.
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BL
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774603
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b594598
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| Main Entry
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edited by Ridvan Berber.
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Methods of Model Based Process Control\ edited by Ridvan Berber.
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| Publication Statement
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Dordrecht : Springer Netherlands : Imprint : Springer, 1995
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| Series Statement
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NATO ASI series., Series E,, Applied sciences ;, 293.
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(840 pages)
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| ISBN
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9401040613
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: 9401101353
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: 9789401040617
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: 9789401101356
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| Contents
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Preface. Part I: Process Modeling, Dynamic Simulation and Identification. Towards a Process Modeling Technology; W. Marquardt. Numerical Methods for the Simulation of Differential-Algebraic Process Models; W. Marquardt. Process Identification Techniques; S. Saelid. Input/Output Modeling for Process Control; T. R. Holcomb, C. A. Rhodes, M. Morai. Part II: Robust Process Control. Frequency Domain Methods for Analysis and Design - I. H-Infinity Methods and Robust Control; S. Skogestad. Frequency Domain Methods for Analysis and Design - II. Input-Output Controllability Analysis of SISO Systems; S. Skogestad. Mp Tuning and Synthesis; M. Laiseca, C. B. Brosilow. Robust Control of Linear Time-Varying Systems with Constraints; A. Zheng, M. Morai. New Results on Robust Controller Design for Chemical Processes; A. Palazogammalu, J. A. Romagnoli. Analysis and Synthesis Methods for Robust Model Predictive Control; H. Genceli, P. Vuthandam, M. Nikolaou. Attainable Performance in LQG Control; B. Lie. Part III: Advances in Model Predictive Control. State-Space Interpretation of Model Predictive Control; J. H. Lee, M. Morari, C. E. Garcia. Topics in Model Predictive Control; E. S. Meadows, J. B. Rawlings. Nonlinear Moving Horizon State Estimation; K. R. Muske, J. B. Rawlings. Optimization in Model Predictive Control; D. Q. Mayne. Internal Model Predictive Control; E. Coulibaly, S. Maiti, C. B. Brosilow. Adaptive Model Predictive Control; M. V. Le Lann, M. Cabassud, G. Casamatta. Control of Batch Reactors: A Review; R. Berber. Real-Time Optimization and Model-Based Control of Polymer Reactors; C. Kiparissides, E. Papadopoulos, J. Morris. Part IV: Nonlinear Model Predictive Control. Control of Nonlinear Systems Using Input Output Information; Y. Arkun, E. Hernandez. A Stability Analysis of Nonlinear Model Predictive Control; P. B. Sistu, B. W. Bequette. The Design of Nonlinear Predictive Controllers: Application to a Drug Infusion System; R. S. Gopinath, B. W. Bequette. Nonlinear Model Predictive Control Using Neural Net Plant Models; A. Draeger, S. Engell. Part V: Industrial Applications. An Industrial Perspective on the Evolution of Control Technology; C. R. Cutler. Modular Multivariable Control of the Shell Heavy Oil Fractionator; T. L. Chia, C. B. Brosilow. An Industrial Implementation of a Model Based Control Strategy; J. L. Figueroa, O. E. Agamennoni, G. W. Barton, J. A. Romagnoli, J. B. Lear. Model Predictive Control of a Gas Oil Water Separator Train; S. Stokke, S. Strand, D. Sjong. Integrating Information, Management and Control in Process Industries; C. Han, G. Stephanopoulos. Part VI: Fuzzy Control. Fuzzy Control An Alternative to Model-Based Control? S. Engell, T. Heckenthaler. Self-Learning Model-Based Fuzzy Controller; B. E. Postlethwaite. Fuzzy Control of Distillation Columns with and without Side Streams; C. Remberg, G. Fieg, G. Wozny, F. N. Fett. Appendices. I: Titles of Poster Presentations. II: Some Views of the Contributors about the Discussed Issues and Research Directions. III: Summary of the Control Research Presented at the Institute: A Cartoon Representation; B. W. Bequette. IV: Keeping with the Tradition:
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| Abstract
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Model based control has emerged as an important way to improve plant efficiency in the process industries, while meeting processing and operating policy constraints. The reader of Methods of Model Based Process Control will find state of the art reports on model based control technology presented by the world's leading scientists and experts from industry. All the important issues that a model based control system has to address are covered in depth, ranging from dynamic simulation and control-relevant identification to information integration. Specific emerging topics are also covered, such as robust control and nonlinear model predictive control. In addition to critical reviews of recent advances, the reader will find new ideas, industrial applications and views of future needs and challenges. <br/> Audience : A reference for graduate-level courses and a comprehensive guide for researchers and industrial control engineers in their exploration of the latest trends in the area. <br/>
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Chemical process control -- Congresses.
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Chemical process control.
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Ridvan Berber
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Proceedings of the NATO Advanced Study Institute, Antalya, Turkey, August 7--17, 1994
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