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. Owing to the chosen linear-in-parameters model structure, the estimation becomes linear and is aymptotically robust to zero-mean additive disturbances. This paper proposes a generalization and flexible parametrization of the Markov parameter model structure, removes the existing limitations in the case of systems with low or zero damping, extends the formulation to MIMO systems and analyses the algorithm establishing identifiability conditions-thus rendering the proposed approach to continuous-time system identification quite general, and particularly attractive for finite-dimensional system identification..