Model Predictive Control: Theory, Practices and Future Challenges

Corrine Wade (Editor)

Series: Mechanical Engineering Theory and Applications
BISAC: TEC009070

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Although industrial processes are inherently nonlinear, many contributions for controller design for those plants are based on the assumption of a linear model of the system. However, in some cases it is difficult to represent a given process using a linear model. Model Predictive Control (MPC) is an optimal control approach which can effectively deal with constraints and multivariable processes in industries. Because of its advantages, MPC has been widely applied in automotive and process control communities. This book discusses the theory, practices and future challenges of model predictive control.

(Imprint: Nova)

Preface

Chapter 1 - Model Predictive Control with Input and Output Constraints (pp. 1-40)
Alessandro Serpi, Gianluca Gatto, Alfonso Damiano and Ignazio Marongiu (Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, Cagliari, Italy)

Chapter 2 - A Model Predictive Control for an Aggregate Actuator with a Self-Tuning Initial Condition Procedure in Combustion Engines (pp. 41-60)
Paolo Mercorelli, Benedikt Haus and Nils Werner (Institute of Product and Process Innovation, Leuphana University of Lüneburg, Volgershall, Lüneburg, Germany, and others)

Chapter 3 - A Decoupled MPC Using a Geometric Approach and Feedforward Action for Motion Control in Robotino (pp. 61-76)
Daniel Strassberger and Paolo Mercorelli (Institute of Product and Process Innovation, Leuphana University of Lüneburg, Volgershall, Lüneburg, Germany)

Chapter 4 - Robust Model Predictive Control of Hammerstein Systems (pp. 77-98)
Silvina Inés Biagiola and José Luis Figueroa (Departamento de Ingenierýa Electrica y de Computadoras, IIIE (UNS-CONICET), Bahýa Blanca, Argentina)

Chapter 5 - Model Predictive Control in Hilbert Space (pp. 99-116)
A. I. Propoi (Institute of Systems Analysis, Russian Academy of Sciences, Moscow, Russia)

Chapter 6 - Coordinated Flexible Distributed Model Predictive Control (pp. 117-144)
Yi Zheng and Shaoyuan Li (School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China)

Chapter 7 - Feasible Set In Model Predictive Control (pp. 145-154)
A. I. Propoi (Institute of Systems Analysis, Russian Academy of Sciences, Moscow, Russia)

Index

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