Optimal Design of Uncertain Complex Dynamical Systems

Abstract

Recent advances in simulation and computation call for innovative modeling and design approaches for engineered systems that can account for the complexities and uncertainties in the system description in design processes. In this research we consider the problem of optimal design of uncertain complex systems. A framework has been developed for the optimal design of complex dynamical systems that incorporates model uncertainties directly into the design objective. A class of systems that exhibit complex behavior that can be approximated by a hybrid system is considered. The discrete behavior evolves on a slow time scale and can be modeled as a hidden Markov process. A model reduction technique that captures all critical aspects ol the discrete component has been developed. Data driven identification techniques have been developed for identification of the hidden Markov model. Furthermore, system identification is used for the characterization of fast time scale dynamics of the hybrid components resulting in reduced models that are suitable for design. The novelty ol the approach lies in hybrid system modeling approach.

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Document Details

Document Type
Technical Report
Publication Date
Nov 14, 2008
Accession Number
ADA586702

Entities

People

  • Thordur Runolfsson

Organizations

  • University of Oklahoma

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Clustering
  • Complex Systems
  • Computational Science
  • Decomposition
  • Hidden Markov Models
  • Identification
  • Markov Chains
  • Markov Models
  • Markov Processes
  • Models
  • Physics
  • Probabilistic Models
  • Probability
  • Students
  • Uncertainty
  • Wind Turbines

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Software Engineering