Optimized tuner selection for engine performance estimation

US8386121B1 · kind B1 · utility

Assignee

• The United States of America as represented by the Administrator of the National Aeronautics and Space Administration · US

Inventors

• Donald L. Simon · Olmsted Falls, US
• Sanjay Garg · Savanuru, IN

Key dates

• Filing date: Jun 2, 2010
• Grant date: Feb 26, 2013
• Expiry date: Aug 27, 2031

Classification

• Technology area (CPC G): Physics
• CPC primary: G01M15/02
• WIPO field: Measurement
• WIPO sector: Instruments

Abstract

A methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. Theoretical Kalman filter estimation error bias and variance values are derived at steady-state operating conditions, and the tuner selection routine is applied to minimize these values. The new methodology yields an improvement in on-line engine performance estimation accuracy.