In this paper, a novel approach to center of gravity estimation for an aircraft will be investigated. Three separate algorthms have been developed using a physics based approach to optimize center of gravity estimates under dynamic loading conditions on a vehicle. Rigid body motion relationships and aircraft dynamics will be exposed to create estimates of numerous aircraft parameters. Errors between estimates and sensor measurements provide the basis for center of gravity improvement. The first algorithm involves calculation and elimination of imposed loading on the airframe, allowing for determination of new attitude estimates. The second algorithm requires comparison of aircraft estimates of position and velocity in the Earth-fixed coordinate system to GPS and INS information. The third algorithm includes analysis using air data measurements available from onboard sensors to calculated estimates. These innovative approaches to center of gravity estimation remove dependence on detailed and expensive aerodynamic models that require level and steady flight conditions for operation, and limit the introduction of human error that occurs in hand calculation methods. Such improved knowledge can improve aircraft control response, reduce conservative safety factors placed on airframe fatigue calculators, and ensure safe loading scenarios throughout the flight envelope. Simulations using measurement collections from a high performance aircraft for each individual algorithm were performed and included. Additionally a combined algorithm incorporating all three methods was simulated. Each resulted in successful center of gravity localization.
[1]
Ryan M. Weisman,et al.
Robust longitudinal rate gyro bias estimation for reliable pitch attitude observation through utilization of a displaced accelerometer array
,
2008
.
[2]
William T. Scorse.
Two dimensional rate gyro bias estimation for precise pitch and roll attitude determination utilizing a dual arc accelerometer array
,
2010
.
[3]
T. G. Gainer,et al.
Summary of transformation equations and equations of motion used in free flight and wind tunnel data reduction and analysis
,
1972
.
[4]
R. Hibbeler.
Engineering Mechanics: Dynamics
,
1986
.
[5]
Robert J. Mack.
A Rapid Empirical Method for Estimating the Gross Takeoff Weight of a High Speed Civil Transport
,
1999
.
[6]
Moshe Idan,et al.
IN-FLIGHT WEIGHT AND BALANCE IDENTIFICATION USING NEURAL NETWORKS
,
2004
.
[7]
Ryan M. Weisman,et al.
Longitudinal Rate Gyro Bias and Pitch Attitude Estimation Utilizing an Accelerometer Array
,
2009
.
[8]
Mark Costello,et al.
In-Flight Estimation of Helicopter Gross Weight and Mass Center Location
,
2009
.