A dynamic design approach using the Kalman filter for uncertainty management. Issue 2 (4th May 2017)

• Record Type: Journal Article
• Authors: Keshavarzi, Elham; McIntire, Matthew; Hoyle, Christopher
• Editors: Ghanem, Roger; Du, Xiaoping
• Is Part Of: AI EDAM. Volume 31:Issue 2(2017)
• Page Start: 161
• Page End: 172
• Publication Date: 2017-05-04
• Subjects: Design Optimization, -- Engineering Systems, -- Kalman Filter, -- Resilience, -- Uncertainty; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060417000051
• Languages: English
• ISSNs: 0890-0604

Abstract

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.