Subspace Identification for Linear Systems focuses on the  theory, implementation and applications of subspace  identification algorithms for linear time-invariant finite-  dimensional dynamical systems. These algorithms allow for a fast,  straightforward and accurate determination of linear multivariable  models from measured input-output data.  The theory of subspace identification algorithms is presented in  detail. Several chapters are devoted to deterministic, stochastic and  combined deterministic-stochastic subspace identification algorithms.  For each case, the geometric properties are stated in a main  'subspace' Theorem. Relations to existing algorithms and literature  are explored, as are the interconnections between different subspace  algorithms. The subspace identification theory is linked to the theory  of frequency weighted model reduction, which leads to new  interpretations and insights.   The implementation of subspace identification algorithms is  discussed in terms of the robust and computationally efficient RQ and  singular value decompositions, which are well-established algorithms  from numerical linear algebra. The algorithms are implemented in  combination with a whole set of classical identification algorithms,  processing and validation tools in Xmath's ISID, a commercially  available graphical user interface toolbox. The basic subspace  algorithms in the book are also implemented in a set of Matlab files  accompanying the book.   An application of ISID to an industrial glass tube manufacturing  process is presented in detail, illustrating the power and  user-friendliness of the subspace identification algorithms and of  their implementation in ISID. The identified model allows for an  optimal control of the process, leading to a significant enhancement  of the production quality. The applicability of subspace  identification algorithms in industry is further illustrated with the  application of the Matlab files to ten practical problems. Since all  necessary data and Matlab files are included, the reader can easily  step through these applications, and thus get more insight in the  algorithms.   Subspace Identification for Linear Systems is an important  reference for all researchers in system theory, control theory, signal  processing, automization, mechatronics, chemical, electrical,  mechanical and aeronautical engineering.

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