This book offers rigorous, yet practical, methods for the design of networked and distributed predictive control systems, including new techniques for system design, insight into likely issues and challenges, and integrated exposition of novel research topics.

Networked and Distributed Predictive Control presents rigorous, yet practical, methods for the design of networked and distributed predictive control systems the first book to do so. The design of model predictive control systems using Lyapunov-based techniques accounting for the influence of asynchronous and delayed measurements is followed by a treatment of networked control architecture development. This shows how networked control can augment dedicated control systems in a natural way and takes advantage of additional, potentially asynchronous and delayed measurements to maintain closed loop stability and significantly to improve closed-loop performance. The text then shifts focus to the design of distributed predictive control systems that cooperate efficiently in computing optimal manipulated input trajectories that achieve desired stability, performance and robustness specifications but spend a fraction of the time required by centralized control systems. Key features of this book include: • new techniques for networked and distributed control system design; • insight into issues associated with networked and distributed predictive control and their solution; • detailed appraisal of industrial relevance using computer simulation of nonlinear chemical process networks and wind- and solar-energy-generation systems; and • integrated exposition of novel research topics and rich resource of references to significant recent work. A full understanding of Networked and Distributed Predictive Control requires a basic knowledge of differential equations, linear and nonlinear control theory and optimization methods and the book is intended for academic researchers and graduate students studying control and for process control engineers. The constant attention to practical matters associated with implementation of the theory discussed will help each of these groups understand the application of the book's methods in greater depth.

Shows the reader the state of the art in networked and distributed predictive control. Demonstrates mathematically rigorous methods that take various practical issues explicitly into account. Provides the practising process engineer with many industrially interesting examples that can be easily modified to specific applications. Includes supplementary material: sn.pub/extras

Texte du rabat

Networked and Distributed Predictive Control presents rigorous, yet practical, methods for the design of networked and distributed predictive control systems. The design of model predictive control systems using Lyapunov-based techniques to account for the influence of asynchronous and delayed measurements is followed by a treatment of networked control architecture development. This shows how networked control can augment dedicated control systems in a natural way and takes advantage of additional, potentially asynchronous and delayed measurements to maintain closed loop stability and significantly to improve closed-loop performance. The text then shifts focus to the design of distributed predictive control systems that cooperate efficiently in computing optimal manipulated input trajectories that achieve desired stability, performance and robustness specifications but utilize a fraction of the time required by centralized control systems. Key features of this book include:

· new techniques for networked and distributed control system design;

· insight into issues associated with networked and distributed predictive control problems;

· detailed appraisal of industrial relevance using computer simulation of nonlinear chemical process networks and wind- and solar-energy-generation systems; and · integrated exposition of novel research topics and rich resource of references to significant recent work. A full understanding of Networked and Distributed Predictive Control requires a basic knowledge of differential equations, linear and nonlinear control theory and optimization methods and the book is intended for academic researchers and graduate students studying control as well as for process control engineers. The constant attention to practical matters associated with implementation of the theory discussed will help researchers and engineersunderstand the application of the book's methods in greater depth.

Contenu
Introduction.- Lyapunov-based Model Predictive Control.- Networked Predictive Process Control.- Distributed Model Predictive Control.- Sequential and Iterative Distributed Model Predictive Control.- Multirate Distributed Model Predictive Control.