Introduction to Model Predictive Control for Discrete-time Dynamical Systems: River Publishers Series in Automation, Control and Robotics
Autor Jun Chenen Limba Engleză Hardback – 23 dec 2026
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.
Preț: 669.25 lei
Preț vechi: 962.35 lei
-30% Precomandă
Puncte Express: 1004
Carte nepublicată încă
Livrare prin curier în România Precomanda se expediază când titlul devine disponibil.
Transport gratuit pentru acest produs Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.
Doresc să fiu notificat când acest titlu va fi disponibil:
Se trimite...
Specificații
ISBN-13: 9788743813033
ISBN-10: 8743813038
Pagini: 230
Ilustrații: 52
Dimensiuni: 156 x 234 mm
Ediția:1
Editura: River Publishers
Colecția River Publishers
Seria River Publishers Series in Automation, Control and Robotics
ISBN-10: 8743813038
Pagini: 230
Ilustrații: 52
Dimensiuni: 156 x 234 mm
Ediția:1
Editura: River Publishers
Colecția River Publishers
Seria River Publishers Series in Automation, Control and Robotics
Public țintă
Academic, Postgraduate, and Professional Practice & DevelopmentCuprins
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
Notă biografică
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.
Descriere
This book is an accessible, application-driven introduction to model predictive control for discrete-time systems. This book bridges theory and practice, showing how to design robust MPC algorithms that handle constraints and real-world complexity, with clear explanations and engineering-focused examples.