Distributed Model Predictive Control For Plant Wide Systems
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Distributed Model Predictive Control For Plant Wide Systems
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Author : Shaoyuan Li
language : en
Publisher: John Wiley & Sons
Release Date : 2017-05-02
Distributed Model Predictive Control For Plant Wide Systems written by Shaoyuan Li and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-02 with Science categories.
DISTRIBUTED MODEL PREDICTIVE CONTROL FOR PLANT-WIDE SYSTEMS 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.
Distributed Model Predictive Control Made Easy
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Author : José M. Maestre
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-10
Distributed Model Predictive Control Made Easy written by José M. Maestre and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-10 with Technology & Engineering categories.
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.
Recent Advances In Model Predictive Control
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Author : Timm Faulwasser
language : en
Publisher: Springer Nature
Release Date : 2021-04-17
Recent Advances In Model Predictive Control written by Timm Faulwasser and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-17 with Science categories.
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.
Coordination Techniques For Distributed Model Predictive Control
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Author : Padideh Ghafoor Mohseni
language : en
Publisher:
Release Date : 2013
Coordination Techniques For Distributed Model Predictive Control written by Padideh Ghafoor Mohseni and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Industrial design coordination categories.
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.
New Directions On Model Predictive Control
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Author : Jinfeng Liu
language : en
Publisher: MDPI
Release Date : 2019-01-16
New Directions On Model Predictive Control written by Jinfeng Liu and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-16 with Engineering (General). Civil engineering (General) categories.
This book is a printed edition of the Special Issue "New Directions on Model Predictive Control" that was published in Mathematics
Price Driven Coordination Of Distributed Model Predictive Controllers
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Author : Bardia Hassanzadeh
language : en
Publisher:
Release Date : 2015
Price Driven Coordination Of Distributed Model Predictive Controllers written by Bardia Hassanzadeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Command and control systems categories.
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.
Assessment And Future Directions Of Nonlinear Model Predictive Control
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Author : Rolf Findeisen
language : en
Publisher: Springer
Release Date : 2007-09-08
Assessment And Future Directions Of Nonlinear Model Predictive Control written by Rolf Findeisen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-08 with Technology & Engineering categories.
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.
Intelligent Optimal Control For Distributed Industrial Systems
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Author : Shaoyuan Li
language : en
Publisher: Springer Nature
Release Date : 2023-06-30
Intelligent Optimal Control For Distributed Industrial Systems written by Shaoyuan Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Technology & Engineering categories.
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.
Distributed Nonlinear State Dependent Model Predictive Control And Estimation For Power Generation Plants
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Author : Salah G. Abokhatwa
language : en
Publisher:
Release Date : 2014
Distributed Nonlinear State Dependent Model Predictive Control And Estimation For Power Generation Plants written by Salah G. Abokhatwa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Centralized model predictive control (MPC) is often considered impractical, inflexible and unsuitable for controlling large-scale systems due to several factors such as large computational effort and difficulty to meet all operational objectives. Therefore, industrial large-scale systems are usually controlled by a distributed control framework. In this thesis, novel sequential nonlinear Distributed Model Predictive Control (DMPC) algorithms for large-scale systems that can handle constraints are proposed. The proposed algorithms are based on nonlinear MPC strategy, which uses a state-dependent nonlinear model to reduce the complexity of solving optimization problem. In this distributed framework, the overall system is divided into several interconnected subsystems and each subsystem is controlled by local MPC. These local MPCs solve convex optimization problem and exchange information via one directional communication channel at each sampling time to achieve the global performance. The proposed algorithms are applied to an industrial power plant model to improve power generation efficiency. A non-linear dynamic model of Combined Cycle Power Plant (CCPP) using the laws of physics was first developed and simulated using decentralized PID controllers. Then, a supervisory controller using linear constrained MPC was designed to tune the performance of the PID controllers. Next, a supervisory centralized nonlinear model predictive control (NMPC) algorithm based on state-dependent models was developed to control the nonlinear plant over a wide operating range. Finally, two sequential DMPC algorithms based on state-dependent models were developed. The lack of states measurement were handled by designing nonlinear distributed state estimation algorithms using state-dependent differential Riccati equation (SDDRE) Kalman filter. Numerical simulation results show that the performance of the proposed DMPC algorithms is close to the centralized NMPC but computationally more efficient compared to the centralized one.
Developments In Model Based Optimization And Control
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Author : Sorin Olaru
language : en
Publisher: Springer
Release Date : 2015-12-23
Developments In Model Based Optimization And Control written by Sorin Olaru and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-23 with Technology & Engineering categories.
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.