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RC Miniature Helicopter Model Estimation via Learn-Lagged Acceleration Criteria


Özge Drama

Semester Thesis, FS13 (10227)

The classical industrial approaches for learning helicopter models from data are based on frequency-domain system-identification procedure. Helicopter dynamics are excited by frequency-sweep input. The data set of frequency responses obtained , characterizes the linearised coupled characteristics of the system. However linear parametrization is unable to capture non-linearity on velocity, angular rates and conservation of momentum. In this semester project we try to overcome this by using a Learn Lagged Acceleration Algorithm that minimizes squared one step prediction error over long time scales. During the course of the project, applicability of this model to the miniature RC Helicopters is investigated. First the estimation algorithm is validated via a virtual model. Then data retrieved from real system is applied to the algorithm and he results were analysed.


Type of Publication:

(13)Semester/Bachelor Thesis

S. M. Huck

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2013:IFA_4458
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