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This paper seeks to provide a brief overview of the somewhat unfamiliar concept underlying system identification especially as it applies to large flexible space structures. Having elaborated on the concept, the authors provide a detailed description of the identification process including model development, its experimental validation and final certification. This discussion is followed by a classification of the different identification methods and a brief evaluation of the potential of existing methodology to address special circumstances of large flexible space structures. The paper concludes by making a few recommendations that are deemed necessary to meet the enormous challenges posed by the deployment or erection of large space structures.

The Task Committee on Methods for Identification of Large Structures in Space was founded in Jul. 1984. The charter of the committee was to prepare a state-of-the-art report on methods of system identification applicable to large space structures (LSS). Funding to support preparation of the report was received in Aug. 1985 from the Air Force Rocket Propulsion Laboratory (now the Air Force Astronautics Laboratory), in the form of a contract to the ASCE. The report was completed, and published by AFRPL in Sep. 1986. The Task Committee consisted of ten members, including ASCE and AFRPL representatives. The membership represented Government, Industry, and Universities, and consisted of electrical, mechanical, and civil engineers, with backgrounds in Structural Dynamics, Optimization, and Controls. An effort was made to use consistent terminology and notation throughout the report which would be compatible with the terminology used in both the structures and controls communities.

An improved method is developed for eigenvalues and eigenvectors placement of a closed-loop control system using either state or output feedback. The method basically consists of three steps. First, the singular value of QR decomposition is used to generate an orthonormal basis that spans admissible eigenvector space corresponding to each assigned eigenvalue. Secondly, given a unitary matrix, the eigenvector set which best approximates the given matrix in the least-square sense and still satisfy eigenvalue cosntraints is determined. Thirdly, a unitary matrix is sought to minimize the error between the unitary matrix and the assignable eigenvector matrix. For use as the desired eigenvector set, two matrices, namely, the open-loop eigenvector matrix and its closest unitary matrix are proposed. The latter matrix generally encourages both minimum conditioning and control gains. In addition, the algorithm is formulated in real arithmetic for efficient implementation. To illustrate the basic concepts, numerical examples are included.