Event-based Model Predictive Control

Name: Event-based Model Predictive Control
Price: 128 USD
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ISBN: 9781394367788

by Bing Zhu, Zhigang Luo, Xiangyu Meng, Zongyu Zuo

1st Edition

Publisher: Wiley-IEEE Press

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Electrical Engineering

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Book Details

Print ISBN	9781394367788
eText ISBN	9781394367795
Publisher	Wiley-IEEE Press
Publishing Year	2026
Edition	1st Edition
Language	English
Pages	176

Event-based Model Predictive Control, 1st Edition, presents modern control methodologies designed to regulate dynamic processes while minimizing computational and communication overhead. Published by Wiley-IEEE Press in 2026, this 176-page volume addresses fundamental topics in event-driven predictive control. The text is co-authored by Bing Zhu, Zhigang Luo, Xiangyu Meng, and Zongyu Zuo.

The authors formulate a two-stage predictive event-triggered MPC strategy tailored for continuous nonlinear systems experiencing external disturbances. For discrete operational settings, the text sets forth an event-based non-periodic interval sampling MPC design that manages disturbed discrete nonlinear models. Additionally, the material addresses formation control tasks involving multi-agent vehicle systems subject to nonholonomic constraints.

To validate theoretical formulations, the volume supplies quantitative analysis evaluating computation, communication, and energy efficiency improvements across various scenarios. These analytical tools offer structured guidance for students, researchers, and practitioners in control engineering who work on resource-conscious automation projects.

Chapter 1: Introduction
- • 1.1 Background and Motivation
- • 1.2 Model Predictive Control
- • 1.3 MPC in the Presence of Uncertainties
- • 1.4 Event-based MPC
- • 1.5 Book Outline
Chapter 2: Two-phase Event-based MPC for Continuous-time Systems
- • 2.1 Problem Statement
- • 2.2 Two-phase Event-triggered MPC Algorithm
- • 2.2.1 Optimization Formulation
- • 2.2.2 Event-based α − β Strategy
- • 2.2.3 Generalization of the α − β Strategy
- • 2.3 Feasibility and Stability
- • 2.3.1 Recursive Feasibility of Optimization
- • 2.3.2 Stability of the Closed-loop System
- • 2.4 Simulation Examples
- • 2.4.1 Undamped Oscillator
- • 2.4.2 Simplified Spring-damper Element in Vehicle Suspension System
- • 2.5 Conclusion
Chapter 3: Event-triggered MPC for Discrete-time Systems with Aperiodic Sampling
- • 3.1 Problem Statement
- • 3.1.1 Plant to Be Controlled
- • 3.1.2 Formulation of Optimization
- • 3.2 Aperiodic Triggering Mechanism
- • 3.2.1 Triggering Mechanism
- • 3.2.2 Stability and Feasibility
- • 3.3 Improved Aperiodic Triggering Mechanism
- • 3.3.1 Statement of Improved Triggering Mechanism
- • 3.3.2 Feasibility and Stability Concerning the Improved Aperiodic Triggering Mechanism
- • 3.4 A Simulation Example
- • 3.5 Conclusion
Chapter 4: Composite Event-triggered MPC based on Disturbance Compensation
- • 4.1 Problem Statement
- • 4.2 Composite Event-triggered MPC Mechanism
- • 4.2.1 Disturbance Compensation Controller Design
- • 4.2.2 Model Predictive Controller Design
- • 4.2.3 Composite Event-triggered MPC
- • 4.2.4 Event-triggered Mechanism with Estimation
- • 4.3 Feasibility and Stability
- • 4.3.1 Recursive Feasibility
- • 4.3.2 Closed-loop Stability
- • 4.4 A Simulation Example
- • 4.5 Conclusion
Chapter 5: Event-triggered MPC with Periodic Sampling for Multi-agent Systems
- • 5.1 Problem Statement
- • 5.2 Distributed MPC Design
- • 5.2.1 Terminal Set and Auxiliary Terminal Control Design
- • 5.2.2 Distributed MPC Framework
- • 5.3 Periodic Event-triggering Mechanism Design
- • 5.4 Feasibility and Stability
- • 5.5 A Simulation Example
- • 5.6 Conclusion
Chapter 6: Concluding Remarks and Future Directions