Introduction to Model Predictive Control for Discrete-time Dynamical Systems

1. Auflage 2026
ISBN: 978-87-438-1303-3
Verlag: River Publishers

Buch, Englisch, 230 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: River Publishers Series in Automation, Control and Robotics

Optimize. Constrain. Control.

Model predictive control (MPC) has revolutionized modern engineering. This book offers a streamlined, accessible guide to MPC, specifically optimized for discrete-time systems.

We bridge the gap between complex mathematical theory and practical engineering reality. Through detailed explanations and real-world examples, you will learn to build robust algorithms that handle complex constraints with ease.

Key features:

Clarity first: Designed for students and experts alike.

Application-driven: Real-world problems, not just theoretical proofs.

Discrete-time focus: Tailored for modern digital implementation.

Equip yourself with the expertise to tackle the most demanding control challenges in industry today.

Zielgruppe

Academic, Postgraduate, and Professional Practice & Development

Autoren/Hrsg.

Chen, Jun

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur

Inhaltsverzeichnis

I Preliminaries 1 Introduction to Control Systems 2 Linear Systems 3 Numerical Optimization II Linear MPC 4 Linear Quadratic Regulator 5 Linear Model Predictive Control III Nonlinear MPC 6 Nonlinear Model Predictive Control 7 State Estimation Appendix Linear Algebra

Über Autor(innen)

Jun Chen received his bachelor’s degree in automation from Zhejiang University, Hangzhou China, in 2009, and Ph.D. in electrical engineering from Iowa State University, Ames IA, USA, in 2014. He was with Idaho National Laboratory from 2014 to 2016 and with General Motors from 2017 to 2020. Dr. Chen joined Oakland University in 2020, where he is currently an associate professor at the ECE department. His research interests include advanced control and optimization, model predictive control, artificial intelligence, and stochastic hybrid systems, with applications in intelligent vehicles, robotics, and energy systems. Dr. Chen is a recipient of the NSF Career Award, the Best Paper Award from IEEE Transactions on Automation Science and Engineering, the Best Paper Award from IEEE International Conference on Electro Information Technology, the Best Paper Award from IEEE Cyber Awareness & Research Symposium, the New Investigator Research Excellence Award and Outstanding Graduate Mentor Award from Oakland University, the Publication Achievement Award from Idaho National Laboratory, the Research Excellence Award from Iowa State University, and the Outstanding Student Award from Zhejiang University. He is currently a Senior Member of the IEEE.