Model Predicitve Control

by Basil Kouvaritakis (Author), Mark Cannon (Author)
Equips the student to deal with broad classes of system uncertainties with the first textbook treatment of stochastic predictive control
Gives the student an up-to-date source on robust predictive control including details of ten years’ of developments
Illustrates the tutorial material in each chapter with worked examples
Problems and solutions are provided for many of the chapters
Exposes students and practitioners to important new probabilistic applications of model predictive control

Tensor Product Model Transformation in Polytopic Model-Based Control offers a new perspective of control system design. Instead of relying solely on the formulation of more effective LMIs, which is the widely adopted approach in existing LMI-related studies, this cutting-edge book calls for a systematic modification and reshaping of the polytopic convex hull to achieve enhanced performance.

This monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of process system engineering and control engineering.

The book presents some recent specialized theoretical and practical works in the field of process control based on the model predictive control (MPC) method. It includes seven chapters that present studies on the application of MPC in various technical processes, such as the atmospheric plasma spray process, permanent magnet synchronous motors, monitoring of the pose of a walking person, monitoring of the heat treatment process of raw materials, discrete event processes, control of passenger vehicles, and natural gas sweetening processes.

This monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of process system engineering and control engineering.