Robust Model Predictive Control for Large-Scale Manufacturing Systems subject to Uncertainties

Robust Model Predictive Control for Large-Scale Manufacturing Systems subject to Uncertainties
Title Robust Model Predictive Control for Large-Scale Manufacturing Systems subject to Uncertainties PDF eBook
Author Jens Tonne
Publisher
Pages
Release 2018
Genre
ISBN 9783737604499

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Robust Model Predictive Control for Large-Scale Manufacturing Systems subject to Uncertainties

Robust Model Predictive Control for Large-Scale Manufacturing Systems subject to Uncertainties
Title Robust Model Predictive Control for Large-Scale Manufacturing Systems subject to Uncertainties PDF eBook
Author Jens Tonne
Publisher kassel university press GmbH
Pages 251
Release 2018-01-19
Genre
ISBN 3737604487

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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

Large Scale Optimization in Supply Chains and Smart Manufacturing
Title Large Scale Optimization in Supply Chains and Smart Manufacturing PDF eBook
Author Jesús M. Velásquez-Bermúdez
Publisher Springer Nature
Pages 297
Release 2019-09-06
Genre Mathematics
ISBN 303022788X

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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

Model Predictive Control in the Process Industry
Title Model Predictive Control in the Process Industry PDF eBook
Author Eduardo F. Camacho
Publisher Springer Science & Business Media
Pages 250
Release 2012-12-06
Genre Technology & Engineering
ISBN 1447130081

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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

International Aerospace Abstracts
Title International Aerospace Abstracts PDF eBook
Author
Publisher
Pages 980
Release 1998
Genre Aeronautics
ISBN

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Model Predictive Control

Model Predictive Control
Title Model Predictive Control PDF eBook
Author Basil Kouvaritakis
Publisher Springer
Pages 387
Release 2015-12-01
Genre Technology & Engineering
ISBN 3319248537

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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.

Economic Model Predictive Control

Economic Model Predictive Control
Title Economic Model Predictive Control PDF eBook
Author Matthew Ellis
Publisher Springer
Pages 311
Release 2016-07-27
Genre Technology & Engineering
ISBN 331941108X

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This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes.In addition to being mathematically rigorous, these methods accommodate key practical issues, for example, direct optimization of process economics, time-varying economic cost functions and computational efficiency. Numerous comments and remarks providing fundamental understanding of the merging of process economics and feedback control into a single framework are included. A control engineer can easily tailor the many detailed examples of industrial relevance given within the text to a specific application. The authors present a rich collection of new research topics and references to significant recent work making Economic Model Predictive Control an important source of information and inspiration for academics and graduate students researching the area and for process engineers interested in applying its ideas.