Prefilter Design for Improved Spectral Flatness of Physical Pseudorandom Perturbations

Abstract

System identification techniques have been applied to model human sensorimotor systems for research and diagnostic purposes. Accurate human modeling via system identification requires informative experimental data, which further require informative perturbations applied to the human. Previously, designing ideal perturbations for system identification has been extensively studied. On the other hand, the physical realization of perturbations often produces distorted perturbations, resulting in less accurate system identification and incorrect conclusions about the target dynamic system. However, such issues associated with an uncompensated physical realization process have not been fully resolved in the literature. A notable application is the design and use of the pseudorandom sequence (PRS). This article considers the physical realization of PRS perturbations. The impact of zero-order-hold (ZOH) on perturbation quality in terms of the previously proposed band-limited spectral flatness measure (SFM) is identified. To compensate for the spectral flatness degradation due to ZOH, a reference prefilter designed by the Parks–McClellan algorithm is incorporated into the motion control design. A relationship between the prefilter performance and band-limited SFM is established, based on which a prefilter design procedure that guarantees an SFM lower bound of the physically realized perturbations is proposed. Experimental results demonstrated that the proposed prefilter design method effectively improves the SFM of the physical perturbations.