Download or read book Distributed Model Predictive Control for Plant Wide Systems written by Shaoyuan Li and published by John Wiley & Sons. This book was released on 2017-05-02 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Distributed Model Predictive Control Made Easy written by José M. Maestre and published by Springer Science & Business Media. This book was released on 2013-11-10 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book New Directions on Model Predictive Control written by Jinfeng Liu and published by MDPI. This book was released on 2019-01-16 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "New Directions on Model Predictive Control" that was published in Mathematics

Download or read book Assessment and Future Directions of Nonlinear Model Predictive Control written by Rolf Findeisen and published by Springer. This book was released on 2007-09-08 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred- tive control with respect to both theoretical and application aspects. Over these 30 years, model predictive control for linear systems has been widely applied, especially in the area of process control. However, today’s applications often require driving the process over a wide region and close to the boundaries of - erability, while satisfying constraints and achieving near-optimal performance. Consequently, the application of linear control methods does not always lead to satisfactory performance, and here nonlinear methods must be employed. This is one of the reasons why nonlinear model predictive control (NMPC) has - joyed signi?cant attention over the past years,with a number of recent advances on both the theoretical and application frontier. Additionally, the widespread availability and steadily increasing power of today’s computers, as well as the development of specially tailored numerical solution methods for NMPC, bring thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This has led to a series of new, exciting developments, along with new challenges in the area of NMPC.

Download or read book Recent Advances in Model Predictive Control written by Timm Faulwasser and published by Springer Nature. This book was released on 2021-04-17 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on distributed and economic Model Predictive Control (MPC) with applications in different fields. MPC is one of the most successful advanced control methodologies due to the simplicity of the basic idea (measure the current state, predict and optimize the future behavior of the plant to determine an input signal, and repeat this procedure ad infinitum) and its capability to deal with constrained nonlinear multi-input multi-output systems. While the basic idea is simple, the rigorous analysis of the MPC closed loop can be quite involved. Here, distributed means that either the computation is distributed to meet real-time requirements for (very) large-scale systems or that distributed agents act autonomously while being coupled via the constraints and/or the control objective. In the latter case, communication is necessary to maintain feasibility or to recover system-wide optimal performance. The term economic refers to general control tasks and, thus, goes beyond the typically predominant control objective of set-point stabilization. Here, recently developed concepts like (strict) dissipativity of optimal control problems or turnpike properties play a crucial role. The book collects research and survey articles on recent ideas and it provides perspectives on current trends in nonlinear model predictive control. Indeed, the book is the outcome of a series of six workshops funded by the German Research Foundation (DFG) involving early-stage career scientists from different countries and from leading European industry stakeholders.

Download or read book Plantwide Cooperative Distributed Model Predictive Control written by Brett Taylor Stewart and published by . This book was released on 2010 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Factories of the Future written by Tullio Tolio and published by Springer. This book was released on 2019-02-14 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license.This book presents results relevant in the manufacturing research field, that are mainly aimed at closing the gap between the academic investigation and the industrial application, in collaboration with manufacturing companies. Several hardware and software prototypes represent the key outcome of the scientific contributions that can be grouped into five main areas, representing different perspectives of the factory domain:1) Evolutionary and reconfigurable factories to cope with dynamic production contexts characterized by evolving demand and technologies, products and processes.2) Factories for sustainable production, asking for energy efficiency, low environmental impact products and processes, new de-production logics, sustainable logistics.3) Factories for the People who need new kinds of interactions between production processes, machines, and human beings to offer a more comfortable and stimulating working environment.4) Factories for customized products that will be more and more tailored to the final user’s needs and sold at cost-effective prices.5) High performance factories to yield the due production while minimizing the inefficiencies caused by failures, management problems, maintenance.This books is primarily targeted to academic researchers and industrial practitioners in the manufacturing domain.

Download or read book Intelligent Optimal Control for Distributed Industrial Systems written by Shaoyuan Li and published by Springer Nature. This book was released on 2023-06-30 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Networked and Distributed Predictive Control written by Panagiotis D. Christofides and published by Springer Science & Business Media. This book was released on 2011-04-07 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Coordination Techniques for Distributed Model Predictive Control written by Padideh Ghafoor Mohseni and published by . This book was released on 2013 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Industrial chemical plants are complex, highly integrated systems composed of geographically distributed processing units, linked together by material and energy streams. To ensure efficient operation in such integrated plants, multivariable optimal control methods like MPC are required. Although centralized MPC may provide the best achievable control performance, issues such as lack of flexibility and maintainability make this approach impractical. The general industrial practice to plant-wide MPC is to recognize the distributed structure of the processing units to design a network of decentralized MPCs. Decentralized controllers avoid the disadvantages associated with centralized control at the expense of poorer plant-wide control performance. To improve the performance of decentralized controllers, Distributed MPC (DMPC) methods have become centre of attention in the plant-wide optimal control research community. DMPC methods are divided into two general classes of non-coordinated and coordinated approaches. Coordinated Distributed MPC (CDMPC) networks, which consist of distributed controllers and a coordinator, are able to yield optimal centralized solution under a wide range of conditions. This work addresses systematic development of CDMPC networks for plant-wide MPC of interconnected dynamical processes, by modifying the existing decentralized MPC network and designing coordinator. Goal Coordination, Interaction Prediction Coordination and Modified-Pseudo Model Coordination are the three coordination methods studied in this thesis to alter the network of decentralized linear constrained MPCs into CDMPC network. Convergence accuracy studies are provided for the proposed coordination algorithms. CDMPC networks are also developed to study the impacts of uncertainty on the CDMPC and coordinator design using an individual chance-constrained approach. By modifying the CDMPC and coordinator in the Goal Coordination method, it is shown that choosing efficient numerical strategies can improve convergence performance of the coordination algorithm. A novel linear CDMPC network, which has performance of centralized nonlinear MPC, is presented to address the plant-wide nonlinear MPC problem. Numerical simulations are provided to test performance of the proposed CDMPC networks.

Download or read book Distributed Model Predictive Control with Event Based Communication written by Groß, Dominic and published by kassel university press GmbH. This book was released on 2015-02-25 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Developments in Model Based Optimization and Control written by Sorin Olaru and published by Springer. This book was released on 2015-12-23 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and design; and · applications to bioprocesses, multivehicle systems or energy management. The various contributions cover a subject spectrum including inverse optimality and more modern decentralized and cooperative formulations of receding-horizon optimal control. Readers will find fourteen chapters dedicated to optimization-based tools for robustness analysis, and decision-making in relation to feedback mechanisms—fault detection, for example—and three chapters putting forward applications where the model-based optimization brings a novel perspective. Developments in Model-Based Optimization and Control is a selection of contributions expanded and updated from the Optimisation-based Control and Estimation workshops held in November 2013 and November 2014. It forms a useful resource for academic researchers and graduate students interested in the state of the art in predictive control. Control engineers working in model-based optimization and control, particularly in its bioprocess applications will also find this collection instructive.

Download or read book Distributed Model Predictive Control written by Aswin N. Venkat and published by . This book was released on 2006 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Price Driven Coordination of Distributed Model Predictive Controllers written by Bardia Hassanzadeh and published by . This book was released on 2015 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chemical and petrochemical plants typically integrate a number of geographically distributed operating units, which are physically linked through energy and material streams or inherently coupled via plant-wide constraints. The main drawback of the current decentralized control system is that it fails to consider the interrelations between subsystems, which could usually result in poor performance or even loss of closed-loop stability. Such concerns have motivated various control strategies to tackle these problems. One possibility is to replace the whole network with a centralized control structure. Despite the potential benefits, this renovation would require significant capital cost, increase maintenance costs, and reduce fault tolerance. Another practical approach is a distributed control that aims to improve the performance of a currently installed decentralized network. Distributed model predictive control (DMPC) methods are divide into two general categories: non-coordinated and coordinated schemes. Coordinated DMPC (CDMPC) networks, which consist of distributed controllers and a coordinator, are able to attain an overall optimal solution over a wide range of conditions. The focus of this thesis is to develop on-line strategies for CDMPC systems and overcome existing issues with global convergence and stability of closed-loop systems, under price-driven CDMPC concept. In particular, the main contributions are developing two novel information flow mechanisms for CDMPC of nonlinear systems and proposing a new solution method for CDMPC of linear systems, via a bi-level optimization framework.

Download or read book Model Predictive Control in the Process Industry written by Eduardo F. Camacho and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.

Download or read book Contributions to Event triggered and Distributed Model Predictive Control written by Felix Berkel and published by Logos Verlag Berlin. This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis deals with event-triggered model predictive control (MPC) strategies for constrained networked and distributed control systems. A networked control system usually consists of spatially distributed sensors, actuators and controllers that communicate over a shared communication network. Event-triggered control approaches consider the network utilization in the controller design to provide a compromise between control performance and communication effort. In this thesis a holistic output-based MPC scheme for constrained linear systems with event-triggered communication over the sensor-to-controller and controller-to-actuator channels of a network is presented. The proposed approach can be applied to centralized as well as decentralized setups and handles bounded time-varying sampling intervals and transmission delays for the control of constrained sampled-data systems. In distributed control set-ups the overall plant is decomposed into subsystems which are controlled by local controllers. Different distributed model predictive control (DMPC) approaches with reduced communication effort are presented in this thesis. The first approach is non-iterative and uses event-triggered communication for the exchange of state measurements. In the second approach, an event-triggered cooperation strategy for DMPC based on distributed optimization is introduced. Finally, an economic DMPC scheme for linear periodically time-varying systems which is motivated by two real-world applications, the control of a water distribution network and a medium voltage power grid, is presented.

Download or read book Robust Model Predictive Control for Large Scale Manufacturing Systems subject to Uncertainties written by Jens Tonne and published by kassel university press GmbH. This book was released on 2018-01-19 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large scale manufacturing systems are often run with constant process parameters although continuous and abrupt disturbances influence the process. To reduce quality variations and scrap, a closed-loop control of the process variables becomes indispensable. In this thesis, a modeling and control framework for multistage manufacturing systems is developed, in which the systems are subject to abrupt faults, such as component defects, and continuous disturbances. In this context, three main topics are considered: the development of a modeling framework, the design of robust distributed controllers, and the application of both to the models of a real hot stamping line. The focus of all topics is on the control of the product properties considering the available knowledge of faults and disturbances.