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.
