Author： Molusis John A.
Publisher： AHS International
ISSN： 2161-6027
Source： Journal of the American Helicopter Society, Vol.18, Iss.2, 1973-04, pp. : 12-23
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Abstract
The Bayesian approach to estimation is applied to the identification of stability and control derivatives from helicopter flight test data. The method used for derivative extraction is similar to the extended Kaiman filter; however, the Bayesian approach provides a more general formulation which yields the most probable derivative estimates from the flight data. Helicopter derivative extraction requirements for a six-degree-of-freedom model are analyzed and a procedure is presented which can solve all the problems related to the identification. An a priori derivative guess and variance is required and shown to be of critical importance to successful identification. Stability derivative convergence is examined and found to be an important indication for control inputs and the amount of data required. The method is applied to CH-53A flight test data at both 100 and 150 knot trim conditions. Characteristic roots of the identified derivative models are presented and demonstrate the ability to predict helicopter stability. The effects of data filtering, a priori derivative guess and proper mode excitation are studied using the flight data. The results demonstrate those requirements that are necessary for successful derivative identification.
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