System Identification (SI) is the process of developing or improving a mathematical representation of a physical system ushg experimental data. Accurate SI is often a precursor to sophisticated control algorithms which assume that the 'plant' to be controlled is known. To achieve accurate models, many of the best cunent SI methods require that the size of the identined statespace mode1 be significantly larger than the expected system size, a process calleded overspecification. Large models are impractical for model-based controller design and create numerical difficulties during the SI process. Low order and high accuracy are two codicting requirements for SI.

Appropriate SI methods for on-orbit modehg of lightly-damped flexible spacecraft are established, includùlg methods such as OKID with ERA, Q-Markov CovER, ORSE and Subspace. Tests on Daisy, a flexible spacecraft emulator, demonstrate that these methods exhibit the overspecification problem.

To investigate SI of Iow-order models, model reduction t ethniques are employed. Bdanced mode1 reduction offers promising results for stable models. Since model stability is not guaranteed by many SI methods, three new ap proaches to balanced model reduction axe derived and tested when identified models are unstable.

A new identification approach using OKID and cubic smoothing splines is presented, dowing low-order highly-accurate models to be directly identified. Avoiding impractically large models reduces computational requirements and pot ential for numerical problems.

Augmented SI is an approach that dows existing iineaz system identification techniques to better model non-idealities such as nonIinear Mction. Augmented and standard SI experiments demonstrate that the linear system assumption made throughout this thesis is appropriate for Daisy.