
Model-Based Predictive Control
A Practical Approach
J.A. Rossiter(Author)
CRC Press
1st Edition
Published on 27. June 2003
Book
Hardback
344 pages
978-0-8493-1291-5 (ISBN)
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Description
Model Predictive Control (MPC) has become a widely used methodology across all engineering disciplines, yet there are few books which study this approach. Until now, no book has addressed in detail all key issues in the field including apriori stability and robust stability results. Engineers and MPC researchers now have a volume that provides a complete overview of the theory and practice of MPC as it relates to process and control engineering.
Model-Based Predictive Control, A Practical Approach, analyzes predictive control from its base mathematical foundation, but delivers the subject matter in a readable, intuitive style. The author writes in layman's terms, avoiding jargon and using a style that relies upon personal insight into practical applications.
This detailed introduction to predictive control introduces basic MPC concepts and demonstrates how they are applied in the design and control of systems, experiments, and industrial processes. The text outlines how to model, provide robustness, handle constraints, ensure feasibility, and guarantee stability. It also details options in regard to algorithms, models, and complexity vs. performance issues.
Model-Based Predictive Control, A Practical Approach, analyzes predictive control from its base mathematical foundation, but delivers the subject matter in a readable, intuitive style. The author writes in layman's terms, avoiding jargon and using a style that relies upon personal insight into practical applications.
This detailed introduction to predictive control introduces basic MPC concepts and demonstrates how they are applied in the design and control of systems, experiments, and industrial processes. The text outlines how to model, provide robustness, handle constraints, ensure feasibility, and guarantee stability. It also details options in regard to algorithms, models, and complexity vs. performance issues.
More details
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Control engineers, chemical engineers, mechanical engineers, electrical engineers, industrial engineers, aerospace engineers, professionals in optimal control, feedback control, and robotics
Illustrations
67 s/w Abbildungen, 8 s/w Tabellen
1000 equations; 8 Tables, black and white; 67 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 159 mm
Weight
608 gr
ISBN-13
978-0-8493-1291-5 (9780849312915)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Content
Introduction
Common Linear Models Used in Model Predictive Control
Prediction in Model Predictive Control
Predictive Control-The Basic Algorithm
Examples - Tuning Predictive Control and Numerical Conditioning
Stability Guarantees and Optimising Performance
Closed-Loop Paradigm
Constraint Handling and Feasibility Issues in MPC
Improving Robustness-The Constraint Free Case
The Relationship Between Modelling and the Robustness of MPC
Robustness of MPC During Constraint Handling and Invariant Sets
Optimisation and Computational Efficiency in Predictive Control
Predictive Functional Control
Multirate Systems
Modelling for Predictive Control
Appendices
Conclusion
Common Linear Models Used in Model Predictive Control
Prediction in Model Predictive Control
Predictive Control-The Basic Algorithm
Examples - Tuning Predictive Control and Numerical Conditioning
Stability Guarantees and Optimising Performance
Closed-Loop Paradigm
Constraint Handling and Feasibility Issues in MPC
Improving Robustness-The Constraint Free Case
The Relationship Between Modelling and the Robustness of MPC
Robustness of MPC During Constraint Handling and Invariant Sets
Optimisation and Computational Efficiency in Predictive Control
Predictive Functional Control
Multirate Systems
Modelling for Predictive Control
Appendices
Conclusion