Robust Model Predictive Control For Large Scale Manufacturing Systems Subject To Uncertainties
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Robust Model Predictive Control For Large Scale Manufacturing Systems Subject To Uncertainties
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Author : Jens Tonne
language : de
Publisher:
Release Date : 2018
Robust Model Predictive Control For Large Scale Manufacturing Systems Subject To Uncertainties written by Jens Tonne and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Robust Model Predictive Control For Large Scale Manufacturing Systems Subject To Uncertainties
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Author : Jens Tonne
language : en
Publisher: kassel university press GmbH
Release Date : 2018-01-19
Robust Model Predictive Control For Large Scale Manufacturing Systems Subject To Uncertainties written by Jens Tonne and has been published by kassel university press GmbH this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-19 with categories.
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.
Large Scale Optimization In Supply Chains And Smart Manufacturing
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Author : Jesús M. Velásquez-Bermúdez
language : en
Publisher: Springer Nature
Release Date : 2019-09-06
Large Scale Optimization In Supply Chains And Smart Manufacturing written by Jesús M. Velásquez-Bermúdez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-06 with Mathematics categories.
In this book, theory of large scale optimization is introduced with case studies of real-world problems and applications of structured mathematical modeling. The large scale optimization methods are represented by various theories such as Benders’ decomposition, logic-based Benders’ decomposition, Lagrangian relaxation, Dantzig –Wolfe decomposition, multi-tree decomposition, Van Roy’ cross decomposition and parallel decomposition for mathematical programs such as mixed integer nonlinear programming and stochastic programming. Case studies of large scale optimization in supply chain management, smart manufacturing, and Industry 4.0 are investigated with efficient implementation for real-time solutions. The features of case studies cover a wide range of fields including the Internet of things, advanced transportation systems, energy management, supply chain networks, service systems, operations management, risk management, and financial and sales management. Instructors, graduate students, researchers, and practitioners, would benefit from this book finding the applicability of large scale optimization in asynchronous parallel optimization, real-time distributed network, and optimizing the knowledge-based expert system for convex and non-convex problems.
Model Predictive Control In The Process Industry
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Author : Eduardo F. Camacho
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Model Predictive Control In The Process Industry written by Eduardo F. Camacho 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 2012-12-06 with Technology & Engineering categories.
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.
International Aerospace Abstracts
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Author :
language : en
Publisher:
Release Date : 1998
International Aerospace Abstracts written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Aeronautics categories.
Model Predictive Control System Design And Implementation Using Matlab
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Author : Liuping Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-02-14
Model Predictive Control System Design And Implementation Using Matlab written by Liuping Wang 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 2009-02-14 with Technology & Engineering categories.
Model Predictive Control System Design and Implementation Using MATLAB® proposes methods for design and implementation of MPC systems using basis functions that confer the following advantages: - continuous- and discrete-time MPC problems solved in similar design frameworks; - a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and - a more general discrete-time representation of MPC design that becomes identical to the traditional approach for an appropriate choice of parameters. After the theoretical presentation, coverage is given to three industrial applications. The subject of quadratic programming, often associated with the core optimization algorithms of MPC is also introduced and explained. The technical contents of this book is mainly based on advances in MPC using state-space models and basis functions. This volume includes numerous analytical examples and problems and MATLAB® programs and exercises.
Cad Cam Abstracts
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Author :
language : en
Publisher:
Release Date : 1992
Cad Cam Abstracts written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with CAD/CAM systems categories.
Applied Mechanics Reviews
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Author :
language : en
Publisher:
Release Date : 1948
Applied Mechanics Reviews written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1948 with Mechanics, Applied categories.
Scientific And Technical Aerospace Reports
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Author :
language : en
Publisher:
Release Date : 1994
Scientific And Technical Aerospace Reports written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Aeronautics categories.
Model Predictive Control
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Author : Basil Kouvaritakis
language : en
Publisher: Springer
Release Date : 2015-12-01
Model Predictive Control written by Basil Kouvaritakis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-01 with Technology & Engineering categories.
For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplicative and stochastic model uncertainty. The book provides: extensive use of illustrative examples; sample problems; and discussion of novel control applications such as resource allocation for sustainable development and turbine-blade control for maximized power capture with simultaneously reduced risk of turbulence-induced damage. Graduate students pursuing courses in model predictive control or more generally in advanced or process control and senior undergraduates in need of a specialized treatment will find Model Predictive Control an invaluable guide to the state of the art in this important subject. For the instructor it provides an authoritative resource for the construction of courses.