|Thesis abstract: |
System identification is today a very wide research area, the results of which find application in a very diverse range of fields. While most of the literature on system identification focuses on discrete-time models, in many situations of practical interest (such, as, e.g., aircraft and rotorcraft identification) the direct estimation of the parameters of a continuous-time model from sampled input-output data is an important problem per se, for which dedicated methods and tools have to be employed. Moreover, the problem of identifying models under special circumstances which turn out to be critical in discrete-time, such as the identification of stiff systems or the use of non-equidistantly sampled data make it necessary to develop special algorithms that can deal with these cases. The development of identification methods for continuous-time models is a challenge its own, and has been studied extensively.
In this thesis continuous-time model identification methods are developed with a special focus on the issues of rotorcraft system identification.
Subspace approaches have been mainly studied in the discrete-time domain but in this thesis it will be shown that their charming properties are still valid in the continuous-time domain.