Comparison between event-based MPC and traditional MPC, highlighting unique advantages and supported with detailed examples for key algorithms
Event-based Model Predictive Control delivers comprehensive knowledge on event-based MPC methods by analyzing the characteristics of event triggering mechanisms and model prediction methods to reduce the burden of optimization problems. The book begins with a comprehensive introduction detailing recent advances related to event-based MPC, then discusses different types of event-based MPC applied to various types of systems.
The book provides a quantitative analysis of computation, communication, and energy efficiency gains in various scenarios, highlights new trends such as periodic/aperiodic event triggers and applications in distributed systems, and discusses ongoing challenges and potential research directions. Numerical and key algorithm examples are included throughout to aid in reader comprehension.
Written by a team of experts in the field, this book includes information on:
- A two-stage predictive event-triggered MPC method for disturbed nonlinear continuous systems
- An event-based non-periodic interval sampling MPC method for disturbed discrete nonlinear systems
- A composite event-triggered MPC method based on disturbance compensation for constrained discrete systems with slowly varying disturbances
- A periodic sampling event-triggered distributed MPC method for the formation control task of multi-agent vehicle systems with nonholonomic constraints
This book is a definitive resource on the subject for students, researchers, and practitioners in the field of control engineering.
Table of Contents:
Contributors
Preface
Acknowledgments
Acronyms
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
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 Optimiaztion 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
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
4 Composite Event-Triggered MPC based on Disturbance Compensation
4.1 Problem Statement 8
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
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
6 Concluding remarks and future directions
Bibliography
About the Author :
BING ZHU is an Associate Professor at the School of Automation Science and Electrical Engineering, Beihang University, China P.R.
ZHIGANG LUO is an Engineer at AVIC XI’AN Flight Automatic Control Research Institute, China P.R.
XIANGYU MENG is an Assistant Professor in the Division of Electrical and Computer Engineering at Louisiana State University, USA.
ZONGYU ZUO is a Professor at the School of Automation Science and Electrical Engineering, Beihang University, China P.R.
