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A dynamic design approach using the Kalman filter for uncertainty management

Published online by Cambridge University Press:  04 May 2017

Elham Keshavarzi ,
Matthew McIntire  and
Christopher Hoyle
Elham Keshavarzi*
Affiliation:
Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA
Matthew McIntire
Affiliation:
Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA
Christopher Hoyle
Affiliation:
Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA
*
Reprint requests to: Elham Keshavarzi, Mechanical, Industrial and Manufacturing Engineering, Oregon State University, 204 Rogers Hall, Corvallis, OR 97330, USA. E-mail: keshavae@oregonstate.edu
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Abstract

It is desirable for complex engineered systems to be resilient to various sources of uncertainty throughout their life cycle. Such systems are high in cost and complexity, and often incorporate highly sophisticated materials, components, design, and other technologies. There are many uncertainties such systems will face throughout their life cycles due to changes in internal and external conditions, or states of interest, to the designer, such as technology readiness, market conditions, or system health. These states of interest affect the success of the system design with respect to the main objectives and application of the system, and are generally uncertain over the life cycle of the system. To address such uncertainties, we propose a resilient design approach for engineering systems. We utilize a Kalman filter approach to model the uncertain future states of interest. Then, based upon the modeled states, the optimal change in the design of the system is achieved to respond to the new states. This resilient method is applicable in systems when the ability to change is embedded in the system design. A design framework is proposed encompassing a set of definitions, metrics, and methodologies. A case study of a communication satellite system is presented to illustrate the features of the approach.

Keywords

Design OptimizationEngineering SystemsKalman FilterResilienceUncertainty
Type
Special Issue Articles
Information
AI EDAM , Volume 31 , Special Issue 2: Uncertainty Quantification for Engineering Design , May 2017 , pp. 161 - 172
Copyright
Copyright © Cambridge University Press 2017 

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