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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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

In Chap. 1, the motivation of networked and distributed process control is first introduced and is followed by a discussion on networked and distributed 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.
Panagiotis D. Christofides, Jinfeng Liu, David Muñoz de la Peña

Chapter 2. Lyapunov-Based Model Predictive Control

In Chap. 2, some basic results on Lyapunov-based control, model predictive control and Lyapunov-based model predictive control of nonlinear systems are 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 as the implementation procedures are carefully designed to account for data losses (or asynchronous measurements) and time-varying measurement delays. 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.
Panagiotis D. Christofides, Jinfeng Liu, David Muñoz de la Peña

Chapter 3. Networked Predictive Process Control

In Chap. 3, a two-tier networked control architecture, which naturally augments preexisting, point-to-point control systems with networked control systems 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 asynchronous measurements which involve time-varying measurement delays. Using a nonlinear continuous stirred tank reactor (CSTR) example and a nonlinear reactor–separator example, the two-tier control architecture is demonstrated to be more optimal compared with conventional control systems and to be more robust 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.
Panagiotis D. Christofides, Jinfeng Liu, David Muñoz de la Peña

Chapter 4. Distributed Model Predictive Control: Two-Controller Cooperation

In Chap. 4, a class of distributed control problems is studied. This class of distributed control problems may arise when new control systems which may use networked sensors and actuators are added to already operating control loops designed via model predictive control (MPC) to improve closed-loop performance. To address this control problem, a distributed model predictive control method is introduced where the preexisting control system and the new control system are redesigned/designed via Lyapunov-based MPC. The distributed control design stabilizes the closed-loop system, improves the closed-loop performance and allows handling input constraints. Furthermore, the distributed control design requires that these controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model predictive control design. The distributed control method is extended to include nonlinear systems subject to asynchronous and delayed measurements. Using a nonlinear reactor–separator example, the stability, performance and robustness of the distributed predictive control designs are illustrated.
Panagiotis D. Christofides, Jinfeng Liu, David Muñoz de la Peña

Chapter 5. Distributed Model Predictive Control: Multiple-Controller Cooperation

In Chap. 5, the results of Chap. 4 are extended to distributed model predictive control of large-scale nonlinear systems in which several distinct sets of manipulated inputs are used to regulate the system. Two distributed control architectures designed via Lyapunov-based model predictive control technique are presented. In the first architecture, the distributed controllers use a one-directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the distributed controllers utilize a bidirectional communication strategy, are evaluated in parallel and iterate to improve closed-loop performance. The case in which continuous state feedback is available to all the distributed controllers is first considered and then the results are extended to include large-scale nonlinear systems subject to asynchronous and delayed state feedback. The theoretical results are illustrated through a catalytic alkylation of benzene process example. Moreover, an approach to handle communication disruptions and data losses between the distributed controllers is discussed.
Panagiotis D. Christofides, Jinfeng Liu, David Muñoz de la Peña

Chapter 6. Multirate Distributed Model Predictive Control

In Chap. 6, a multirate distributed model predictive control design for large-scale nonlinear uncertain systems with fast and slowly sampled states is developed. The distributed model predictive controllers are connected through a shared communication network and cooperate in an iterative fashion at time instants in which both fast and slowly sampled measurements are available, to guarantee closed-loop stability. When only local subsystem fast sampled state information is available, the distributed controllers operate in a decentralized fashion to improve closed-loop performance. In the design of the distributed controllers, bounded measurement noise, process disturbances and communication noise are also taken into account. Using a reactor–separator process example, the stability property and performance of the multirate distributed predictive control architecture is illustrated.
Panagiotis D. Christofides, Jinfeng Liu, David Muñoz de la Peña

Chapter 7. Conclusions

In Chap. 7, the main results of each chapter of the book are reviewed. Future research directions in networked and distributed process control are also discussed.
Panagiotis D. Christofides, Jinfeng Liu, David Muñoz de la Peña

Backmatter

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