Accuracy Of Data Acquisition System Research Articles

Continuous-time dynamic system identification with multisine random excitation revisited

Continuous-time dynamic system identification with multisine random excitation revisitedThe paper presents a new, revisited and unified approach to a linear continuous-time dynamic single-input single-output system identification using input and output signal samples acquired with a deterministic constant or random sampling interval. The approach is based on a specially designed identification experiment with excitation of the form of a continuous-time multisine random excitation and digital processing of the corresponding signal samples obtained without analogue antialiasing filtration in the case of disturbances satisfying or not satisfying the Shannon's sampling theorem. Properties of the proposed approach are discussed taking into account nonlinearity of the excitation generation and data acquisition systems with a focus on model identification in the case of input and output signal levels comparable with data acquisition system accuracy. Methods reducing influence of the disturbances (including aliasing) as well as nonlinearities of the excitation generation and data acquisition systems on identification results are proposed, too.

Continuous-Time Dynamic System Identification with Multisine Random Excitation Revisited

Abstract The paper presents a new, revisited and unified approach to a linear continuous-time dynamic single-input single-output system identification using input and output signal samples acquired with a deterministic constant or random sampling interval. The approach is based on a specially designed identification experiment with excitation of the form of a continuous-time multisine random signal and digital processing of the corresponding signal samples obtained without antialiasing analogue filtration in the case of disturbances satisfying or not satisfying the Shannon's sampling theorem. Properties of the proposed approach are discussed taking into account nonlinearity of the excitation generation and data acquisition systems with a focus on model identification in the case of input and output signal levels comparable with data acquisition system accuracy.