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DISTRIBUTED MODEL PREDICTIVE CONTROL FOR PLANT-WIDE SYSTEMS In this book, experienced researchers gave a thorough explanation of distributed model predictive control (DMPC): its basic concepts, technologies, and implementation in plant-wide systems. Known for its error tolerance, high flexibility, and good dynamic performance, DMPC is a popular topic in the control field and is widely applied in many industries. To efficiently design DMPC systems, readers will be introduced to several categories of coordinated DMPCs, which are suitable for different control requirements, such as network connectivity, error tolerance, performance of entire closed-loop systems, and calculation of speed. Various real-life industrial applications, theoretical results, and algorithms are provided to illustrate key concepts and methods, as well as to provide solutions to optimize the global performance of plant-wide systems. Features system partition methods, coordination strategies, performance analysis, and how to design stabilized DMPC under different coordination strategies. Presents useful theories and technologies that can be used in many different industrial fields, examples include metallurgical processes and high-speed transport. Reflects the authors’ extensive research in the area, providing a wealth of current and contextual information. Distributed Model Predictive Control for Plant-Wide Systems is an excellent resource for researchers in control theory for large-scale industrial processes. Advanced students of DMPC and control engineers will also find this as a comprehensive reference text.
In this thesis, several algorithms for distributed model predictive control over digital communication networks with parallel computation are developed and analyzed. Distributed control aims at efficiently controlling large scale dynamical systems which consist of interconnected dynamical systems by means of communicating local controllers. Such distributed control problems arise in applications such as chemical processes, formation control, and control of power grids. In distributed model predictive control the underlying idea is to solve a large scale model predictive control problem in a distributed fashion in order to achieve faster computation and better robustness against local failures. Distributed model predictive control often heavily relies on frequent communication between the local model predictive controllers. However, a digital communication network may induce uncertainties such as a communication delays, especially if the load on the communication network is high. One topic of this thesis is to develop a distributed model predictive control algorithm for subsystems interconnected by constraints and common control goals which is robust with respect to time-varying communication delays.
This book is a printed edition of the Special Issue "New Directions on Model Predictive Control" that was published in Mathematics
This book is inspired by the development of distributed model predictive control of networked systems to save computation and communication sources. The significant new contribution is to show how to design efficient DMPCs that can be coordinated asynchronously with the increasing effectiveness of the event-triggering mechanism and how to improve the event-triggered DMPC for different requirements improvement of control performance, extension to interconnected networked systems, etc. The book is likely to be of interest to the persons who are engaged in researching control theory in academic institutes, the persons who go in for developing control systems in R&D institutes or companies, the control engineers who are engaged in the implementation of control algorithms, and people who are interested in the distributed MPC.
The use of control systems is necessary for safe and optimal operation of industrial processes in the presence of inevitable disturbances and uncertainties. Plant-wide control (PWC) involves the systems and strategies required to control an entire chemical plant consisting of many interacting unit operations. Over the past 30 years, many tools and methodologies have been developed to accommodate increasingly larger and more complex plants. This book provides a state-of-the-art of techniques for the design and evaluation of PWC systems. Various applications taken from chemical, petrochemical, biofuels and mineral processing industries are used to illustrate the use of these approaches. This book contains 20 chapters organized in the following sections: Overview and Industrial Perspective Tools and Heuristics Methodologies Applications Emerging Topics With contributions from the leading researchers and industrial practitioners on PWC design, this book is key reading for researchers, postgraduate students, and process control engineers interested in PWC.
The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems. This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those who want to gain a deeper insight in the wide range of distributed MPC techniques available.
The paradigm of ‘multi-agent’ cooperative control is the challenge frontier for new control system application domains, and as a research area it has experienced a considerable increase in activity in recent years. This volume, the result of a UCLA collaborative project with Caltech, Cornell and MIT, presents cutting edge results in terms of the “dimensions” of cooperative control from leading researchers worldwide. This dimensional decomposition allows the reader to assess the multi-faceted landscape of cooperative control. Cooperative Control of Distributed Multi-Agent Systems is organized into four main themes, or dimensions, of cooperative control: distributed control and computation, adversarial interactions, uncertain evolution and complexity management. The military application of autonomous vehicles systems or multiple unmanned vehicles is primarily targeted; however much of the material is relevant to a broader range of multi-agent systems including cooperative robotics, distributed computing, sensor networks and data network congestion control. Cooperative Control of Distributed Multi-Agent Systems offers the reader an organized presentation of a variety of recent research advances, supporting software and experimental data on the resolution of the cooperative control problem. It will appeal to senior academics, researchers and graduate students as well as engineers working in the areas of cooperative systems, control and optimization.
This book offers a comprehensive, easy-to-understand overview of receding-horizon control for nonlinear networks. It presents novel general strategies that can simultaneously handle general nonlinear dynamics, system constraints, and disturbances arising in networked and large-scale systems and which can be widely applied. These receding-horizon-control-based strategies can achieve sub-optimal control performance while ensuring closed-loop stability: a feature attractive to engineers. The authors address the problems of networked and distributed control step-by-step, gradually increasing the level of challenge presented. The book first introduces the state-feedback control problems of nonlinear networked systems and then studies output feedback control problems. For large-scale nonlinear systems, disturbance is considered first, then communication delay separately, and lastly the simultaneous combination of delays and disturbances. Each chapter of this easy-to-follow book not only proposes and analyzes novel control algorithms and/or strategies, but also rigorously develops provably correct design conditions. It also provides concise, illustrative examples to demonstrate the implementation procedure, making it invaluable both for academic researchers and engineering practitioners.
This book focuses on the distributed control and estimation of large-scale networked distributed systems and the approach of distributed model predictive and moving horizon estimation. Both principles and engineering practice have been addressed, with more weight placed on engineering practice. This is achieved by providing an in-depth study on several major topics such as the state estimation and control design for the networked system with considering time-delay, data-drop, etc., Distributed MPC design for improving the performance of the overall networked system, which includes several classic strategies for different scenarios, details of the application of the distributed model predictive control to smart grid system and distributed water network. The comprehensive and systematic treatment of theoretical and practical issues in distributed MPC for networked systems is one of the major features of the book, which is particularly suited for readers who are interested to learn practical solutions in distributed estimation and optimization of distributed networked systems. The book benefits researchers, engineers, and graduate students in the fields of chemical engineering, control theory and engineering, electrical and electronic engineering, chemical engineering, and computer engineering, etc.
27th European Symposium on Computer Aided Process Engineering, Volume 40 contains the papers presented at the 27th European Society of Computer-Aided Process Engineering (ESCAPE) event held in Barcelona, October 1-5, 2017. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students, and consultants for chemical industries. - Presents findings and discussions from the 27th European Society of Computer-Aided Process Engineering (ESCAPE) event