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Book synopsis

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 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, encouraging readers to contribute to this advancing field. Numerical and key algorithm examples are included throughout to aid in reader comprehension. Written by a team of experts in the field, Event-based Model Predictive Control includes information on: A two-stage predictive event-triggered MPC method for disturbed nonlinear continuous systems, using a predictive approach to introduce the concept of safety prediction valueAn event-based non-periodic interval sampling MPC method for disturbed discrete nonlinear systemsA composite event-triggered MPC method based on disturbance compensation for constrained discrete systems with slowly varying disturbancesA periodic sampling event-triggered distributed MPC method for the formation control task of multi-agent vehicle systems with nonholonomic constraints Event-based Model Predictive Control is a definitive resource on the subject for students, researchers, and practitioners in the field of control engineering.

Book details

• Full title: Event-based Model Predictive Control
• Author: Bing Zhu, Zhigang Luo, Xiangyu Meng, Zongyu Zuo
• Language: English
• Binding: Hardback
• Number of pages: 176
• EAN: 9781394367788
• ID: 48382188
• Publisher: Not Stated
• Weight: 666 g
• Date of publishing: 17. January 2027

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