IRREDUCIBLE CONTINUOUS MODEL IDENTIFICATION VIA MARKOV PARAMETER-ESTIMATION

This paper presents a new approach to irreducible model identification of continuous-time MIMO systems via Markov parameter estimation. Owin g to the chosen linear-in-parameters model structure, the estimation b ecomes linear and is aymptotically robust to zero-mean additive distur bances. This paper proposes a generalization and flexible parametrizat ion of the Markov parameter model structure, removes the existing limi tations in the case of systems with low or zero damping, extends the f ormulation to MIMO systems and analyses the algorithm establishing ide ntifiability conditions-thus rendering the proposed approach to contin uous-time system identification quite general, and particularly attrac tive for finite-dimensional system identification..