[PDF] The Robust Design Of Complex Systems eBook

Author : Jason C. Slagle
Publisher :
Page : 139 pages
File Size : 31,85 MB
Release : 2007
Category :
ISBN :

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Robust design methodologies are frequently utilized by organizations to develop robust and reliable complex systems. The intent of robust design is to create systems that are insensitive to variations from production, the environment, and time and use. While this process is effective, it can also be very time consuming and resource intensive for an engineering team. In addition, most robust design activity takes place at the detailed design phase, when the majority of the product life cycle cost has already been committed. Addressing robustness and the "ilities" at the architecture level may be more effective because it is the earliest and highest leverage point in the product development process. Furthermore, some system architectures are inherently more robust than others. In this thesis, a framework based on principles is proposed to architect complex systems for type I and II robustness. The principles are obtained by tracing the architectural evolution of the jet engine, which is an extremely complex system that has evolved to high reliability. This framework complements existing robust design methods, while simultaneously incorporating the robustness focus earlier in the product development process.

Author : Benjamin Baxter
Publisher :
Page : pages
File Size : 24,93 MB
Release : 2013
Category :
ISBN :

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Engineered system complexity continues to increase rapidly, concurrent with the requirement for the engineered system to be robust. Robustness is often considered a critical attribute of complex engineered systems, but an exact definition of robustness is not agreed upon within the systems engineering community. Lack of a clear definition, makes it difficult to develop or utilize a quantitative measure of robustness. Having a formal measure for robustness may not be considered necessary, but a lack of a specific measure results in the inability to communicate the desired level of robustness, inability to measure how various options impact robustness, and makes it difficult to measure tradeoffs between robustness and other engineering parameters. The objective of this research is to examine robustness and how it can be attained in systems engineering. In order to accomplish this objective, data from several scientific communities is examined to develop the meaning of robustness. While definitions between and even within each community differ, a key attribute is present in each definition: A robust system needs to maintain its core functions in the presence of internal and external changes. The key component of the characteristic is that each function within a system has its own measure of robustness. When robustness and engineering are discussed, Robust Design must be examined. The scientific community uses variance as its measure for robustness. The Robust Design method has the adverse characteristic of forcing preferences upon the designer. Examining the mean-variance approach with utility theory shows that it imposes an increasingly risk averse position upon the designer. This position may not be compatible with the designer's true risk attitude, causing issues when applying the method. To contend with this issue, a novel utility-based approach is suggested. The approach focuses on generating functional models of the proposed systems, which provide the designer with insight into which perturbations are relevant to the system and subsystems. Additionally this approach incorporates utility theory to allow the designer to convey their preferences. The utility-based approach allows the designer to convey their own preferences, while incorporating steps to ensure the final design is robust. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151048

Author : Sung H. Park
Publisher : World Scientific
Page : 558 pages
File Size : 16,64 MB
Release : 2008
Category : Technology & Engineering
ISBN : 9812778675

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This book is written primarily for engineers and researchers who use statistical robust design for quality engineering and Six Sigma, and for statisticians who wish to know about the wide range of applications of experimental design in industry. It is a valuable guide and reference material for students, managers, quality improvement specialists and other professionals interested in Taguchi's robust design methods as well as the implementation of Six Sigma. This book can also be useful to those who would like to learn about the role of Robust Design within the Six Sigma (Improve phase) methodology and Design for Six Sigma (DFSS) (Optimize) methodology. It combines classical experimental design methods with those of Taguchi's robust designs, demonstrating their prowess in DFSS and suggesting new directions for the development of statistical design and analysis.

Author : Erica Jen
Publisher : Oxford University Press
Page : 306 pages
File Size : 47,62 MB
Release : 2005
Category : Juvenile Nonfiction
ISBN : 0195165330

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'Robust Design' brings together an eminent group of authors in a wide range of fields presenting aspects of robustness in biological, ecological and computational systems.

Author : Da-Wei Gu
Publisher : Springer Science & Business Media
Page : 832 pages
File Size : 34,95 MB
Release : 2005-06-20
Category : Technology & Engineering
ISBN : 9781852339838

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Shows readers how to exploit the capabilities of the MATLAB® Robust Control and Control Systems Toolboxes to the fullest using practical robust control examples.

Author : Kagan Tumer
Publisher : Springer Science & Business Media
Page : 329 pages
File Size : 39,70 MB
Release : 2012-12-06
Category : Mathematics
ISBN : 1441989099

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Many complex systems found in nature can be viewed as function optimizers. In particular, they can be viewed as such optimizers of functions in extremely high dimensional spaces. Given the difficulty of performing such high-dimensional op timization with modern computers, there has been a lot of exploration of computa tional algorithms that try to emulate those naturally-occurring function optimizers. Examples include simulated annealing (SA [15,18]), genetic algorithms (GAs) and evolutionary computation [2,3,9,11,20-22,24,28]. The ultimate goal of this work is an algorithm that can, for any provided high-dimensional function, come close to extremizing that function. Particularly desirable would be such an algorithm that works in an adaptive and robust manner, without any explicit knowledge of the form of the function being optimized. In particular, such an algorithm could be used for distributed adaptive control---one of the most important tasks engineers will face in the future, when the systems they design will be massively distributed and horribly messy congeries ofcomputational systems.

Author : Kim, Dookie
Publisher : IGI Global
Page : 644 pages
File Size : 30,40 MB
Release : 2018-06-15
Category : Technology & Engineering
ISBN : 1522547673

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The disciplines of science and engineering rely heavily on the forecasting of prospective constraints for concepts that have not yet been proven to exist, especially in areas such as artificial intelligence. Obtaining quality solutions to the problems presented becomes increasingly difficult due to the number of steps required to sift through the possible solutions, and the ability to solve such problems relies on the recognition of patterns and the categorization of data into specific sets. Predictive modeling and optimization methods allow unknown events to be categorized based on statistics and classifiers input by researchers. The Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering is a critical reference source that provides comprehensive information on the use of optimization techniques and predictive models to solve real-life engineering and science problems. Through discussions on techniques such as robust design optimization, water level prediction, and the prediction of human actions, this publication identifies solutions to developing problems and new solutions for existing problems, making this publication a valuable resource for engineers, researchers, graduate students, and other professionals.

Author : Bor-Sen Chen
Publisher : CRC Press
Page : 255 pages
File Size : 27,21 MB
Release : 2021-12-23
Category : Mathematics
ISBN : 1000514099

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Most systems in science, engineering, and biology are of partial differential systems (PDSs) modeled by partial differential equations. Many books about partial differential equations have been written by mathematicians and mainly address some fundamental mathematic backgrounds and discuss some mathematic properties of partial differential equations. Only a few books on PDSs have been written by engineers; however, these books have focused mainly on the theoretical stabilization analysis of PDSs, especially mechanical systems. This book investigates both robust stabilization control design and robust filter design and reference tracking control design in mechanical, signal processing, and control systems to fill a gap in the study of PDSs. Robust Engineering Designs of Partial Differential Systems and Their Applications offers some fundamental background in the first two chapters. The rest of the chapters focus on a specific design topic with a corresponding deep investigation into robust H∞ filtering, stabilization, or tracking design for more complex and practical PDSs under stochastic fluctuation and external disturbance. This book is aimed at engineers and scientists and addresses the gap between the theoretical stabilization results of PDSs in academic and practical engineering designs more focused on the robust H∞ filtering, stabilization, and tracking control problems of linear and nonlinear PDSs under intrinsic random fluctuation and external disturbance in industrial applications. Part I provides backgrounds on PDSs, such as Galerkin’s, and finite difference methods to approximate PDSs and a fuzzy method to approximate nonlinear PDSs. Part II examines robust H∞ filter designs for the robust state estimation of linear and nonlinear stochastic PDSs. And Part III treats robust H∞ stabilization and tracking control designs of linear and nonlinear PDSs. Every chapter focuses on an engineering design topic with both theoretical design analysis and practical design examples.