Titlebook: Networked and Distributed Predictive Control; Methods and Nonlinea Panagiotis D. Christofides,Jinfeng Liu,David Muñoz Book 2011 Springer-Ve

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1430-9491 s 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 ap978-1-4471-2648-5978-0-85729-582-8Series ISSN 1430-9491 Series E-ISSN 2193-1577

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Nefarious

1430-9491 e various practical issues explicitly into account..ProvidesNetworked 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 Ly

水土

Book 2011systems – 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 augme

Indicative

Networked Predictive Process Control,ust compared with centralized predictive control systems. The two-tier control architecture is also applied to the problem of optimal management and operation of a standalone wind–solar energy generation system.

Ornament

Lyapunov-Based Model Predictive Control, The presented LMPC designs possess an explicit characterization of the closed-loop system stability region. Using a nonlinear chemical reactor example, it is demonstrated that the presented LMPC approaches are robust to data losses and measurement delays.

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Introduction,ed control architectures with block diagram illustrations as well as a specific chemical process example. Subsequently, previous work on networked and distributed control is reviewed and the objectives and organization of the book are discussed.

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Lyapunov-Based Model Predictive Control,e first reviewed and then two Lyapunov-based model predictive control (LMPC) designs for systems subject to data losses and time-varying measurement delays are presented. In order to provide guaranteed closed-loop stability results, the constraints that define the LMPC optimization problems as well

预示

Networked Predictive Process Control,tems taking advantage of real-time wired or wireless sensor and actuator networks, is presented. The two-tier networked control architecture for systems with continuous and asynchronous measurements is first presented and then the design is extended to include systems with continuous and asynchronou