A Subspace Identification Method Applied on an Hydraulic Testbed

Abstract

Practitioner engineers in both academic and industrial areas, are often faced with the challenge of identifying the model of a given system or process in order to setup a controller or to extract some useful information. Among the existing identification algorithms, those being numerically simple and stable are more attractive for practitioners. This paper deals with identification of state-space models, 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 modest computational complexity compared with other identification 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 identifies the parity space–PS (or null space) from input/output data, and from there, the matrices related to the system through the extended observability matrix and a block triangular Toeplitz matrix. In order to show its capability, the proposed identification technique is applied to an academic test bed that is related to an hydraulic process.