Practitioner engineers in b oth academic and industrial areas, are often faced with the challenge of identifying the mo del of a given system or process in order to setup a controller or to extract some useful information. Among the existing identi fication...
More
Practitioner engineers in b oth academic and industrial areas, are often faced with the challenge of identifying the mo del of a given system or process in order to setup a controller or to extract some useful information. Among the existing identi fication
algorithms, those b eing numerically simple and stable are more attractive for practitioners. This paper deals with identi fication of state-space mo dels, i.e., the state space matrices A, B, C and D for multivariable dynamic systems directly from test data (data-driven). In order
to guarantee numerical reliability and mo dest computational complexity compared with other identi fication techniques, in this paper, we propose a synergistic identification technique based on the principal components analysis (PCA) and subspace identification method (SIM) under white noise assumptions. The proposed technique identi fies the parity space{PS (or null space) from input/output data, and from there, the matrices related to the system throu
Less
