Abstract
System Identification (SI) is a methodology for building mathematical models of dynamic systems from experimental data, i.e., using measurements of the system input/output (IO) signals to estimate the values of adjustable parameters in a given model structure. The process of SI requires some steps, such as measurement of the IO signals of the system in time or frequency domain, selection of a candidate model structure, choice and application of a method to estimate the value of the adjustable parameters in the candidate model structure, validation and evaluation of the estimated model to see if the model is right for the application needs, which should be done preferably with a different set of data, [PS] and [Lj1]. © 2015 Springer International Publishing.

Abstract
In this paper we introduce a recursive subspace system identification algorithm for MIMO linear parameter varying systems driven by general inputs and a white noise time varying parameter vector. The new algorithm is based on a convergent sequence of linear deterministic-stochastic state-space approximations, thus considered a Picard based method. Such methods have proven to be convergent for the bilinear state-space system identification problem. The key to the proposed algorithm is the fact that the bilinear term between the time varying parameter vector and the state vector behaves like a white noise process. Using a linear Kalman filter model, the bilinear term can be efficiently estimated and then used to construct an augmented input vector at each iteration. Since the previous state is known at each iteration, the system becomes linear, which can be identified with a linear-deterministic subspace algorithm such as MOESP, N4SID, or CVA. Furthermore, the model parameters obtained with the new algorithm converge to those of a linear parameter varying model. Finally, the dimensions of the data matrices are comparable to those of a linear subspace algorithm, thus avoiding the curse of dimensionality. © 2007 EUCA.

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