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.
Table of Contents
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
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
Modelling for Predictive Control