Optimization for minimum sensitivity to uncertain parameters

Pritchard, Joselyn I.; Adelman, Howard M.; Sobieszczanski‐Sobieski, Jaroslaw

doi:10.2514/3.13259

Cited by 5 publications

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Optimal experiment design

Emery

Ненарокомов

1998

Meas. Sci. Technol.

141

134

View full text Add to dashboard Cite

Optimal experiment design is the definition of the conditions under which an experiment is to be conducted in order to maximize the accuracy with which the results are obtained. This paper summarizes a number of methods by which the parameters of the mathematical model of the system are estimated and describes the application of the Fisher information matrix. Examples are given for thermal property estimation in which the estimation is affected both by measurement noise, which is present during any experiment, but also by uncertainties in the parameters of the model used to describe the system.

show abstract

Optimal experiment design

Emery

Ненарокомов

1998

Meas. Sci. Technol.

141

134

View full text Add to dashboard Cite

show abstract

Robust Optimization in Discrete Design Space for Constrained Problems

Lee

Park

2002

AIAA Journal

View full text Add to dashboard Cite

Multivariable Isoperformance Methodology for Precision Opto-Mechanical Systems

Weck

Ladislas

2002

43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

View full text Add to dashboard Cite

Precision opto-mechanical systems, such as space telescopes, combine structures, optics and controls in order to meet stringent pointing and phasing requirements. In this context a novel approach to the design of complex, multi-disciplinary systems is presented in the form of a multivariable isoperformance methodology. The isoperformance approach first finds a point design within a given topology, which meets the performance requirements with sufficient margins. The performance outputs are then treated as equality constraints and the nonuniqueness of the design space is exploited by trading key disturbance, plant, optics and controls parameters with respect to each other.Three algorithms (branch-and-bound, tangential front following and vector spline approximation) are developed for the bivariate and multivariable problem. The challenges of large order models are addressed by presenting a fast diagonal Lyapunov solver, apriori error bounds for model reduction as well as a governing sensitivity equation for similarity transformed state space realizations. Specific applications developed with this technique are error budgeting and multiobjective design optimization. The goal of the multiobjective design optimization is to achieve a design which is pareto optimal, such that multiple competing objectives can be satisfied within the performance invariant set. Thus, situations are avoided where very costly and hard-to-meet requirements are levied onto one subsystem, while other subsystems hold substantial margins.An experimental validation is carried out on the DOLCE laboratory testbed. The testbed allows verification of the predictive capability of the isoperformance technique on models of increasing fidelity. A comparison with experimental results, trading excitation amplitude and payload mass, is demonstrated. The predicted performance contours match the experimental data very well at low excitation levels, typical of the disturbance environment on precision opto-mechanical systems. The relevance of isoperformance to space systems engineering is demonstrated with a comprehensive NEXUS spacecraft dynamics and controls analysis. It is suggested that isoperformance is a useful concept in other fields of engineering science such as crack growth calculations in structures. The isoperformance approach enhances the understanding of complex opto-mechanical systems beyond the local neighborhood of a particular point design. Sharon-Leah Brown provided encouragement and professional financial management.

show abstract

Optimization for minimum sensitivity to uncertain parameters

Cited by 5 publications

References 9 publications

Optimal experiment design

Optimal experiment design

Robust Optimization in Discrete Design Space for Constrained Problems

Multivariable Isoperformance Methodology for Precision Opto-Mechanical Systems

Contact Info

Product

Resources

About