Introduction to Model Predictive Control for Discrete-time Dynamical Systems

Introduction to Model Predictive Control for Discrete-time Dynamical Systems

Automation, Control and Robotics

Introduction to Model Predictive Control for Discrete-time Dynamical Systems Forthcoming

Author: Jun Chen, Oakland University, USA

ISBN: 9788743813033 (Hardback) e-ISBN: 9788743813040

Available: December 2026


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.

Model predictive control, discrete-time systems, constrained optimization, optimal control theory, receding horizon control, state-space modeling, real-time optimization, feedback control systems

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