Browsing by Author "Cowan, N. J."
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Open Access Frequency-domain subspace ıdentification of linear time periodic (LTP) systems(Institute of Electrical and Electronics Engineers, 2019) Uyanik, I.; Saranli, U.; Ankaralı, M.; Cowan, N. J.; Morgül, ÖmerThis note proposes a new methodology for subspace-based state-space identification for linear time-periodic (LTP) systems. Since LTP systems can be lifted to equivalent linear time-invariant (LTI) systems, we first lift input-output data from the unknown LTP system as if it was collected from an equivalent LTI system. Then, we use frequency-domain subspace identification methods to find an LTI system estimate. Subsequently, we propose a novel method to obtain a time-periodic realization for the estimated lifted LTI system by exploiting the specific parametric structure of Fourier series coefficients of the frequency-domain lifting method. Our method can be used to both obtain state-space estimates for unknown LTP systems as well as to obtain Floquet transforms for known LTP systems. IEEEItem Open Access Identification of a vertical hopping robot model via harmonic transfer functions(Sage Publications Ltd., 2016) Uyanık, İ.; Ankaralı, M. M.; Cowan, N. J.; Saranlı U.; Morgül, Ö.A common approach to understanding and controlling robotic legged locomotion is the construction and analysis of simplified mathematical models that capture essential features of locomotor behaviours. However, the representational power of such simple mathematical models is inevitably limited due to the non-linear and complex nature of biological locomotor systems. Attempting to identify and explicitly incorporate key non-linearities into the model is challenging, increases complexity, and decreases the analytic utility of the resulting models. In this paper, we adopt a data-driven approach, with the goal of furnishing an input–output representation of a locomotor system. Our method is based on approximating the hybrid dynamics of a legged locomotion model around its limit cycle as a Linear Time Periodic (LTP) system. Perturbing inputs to the locomotor system with small chirp signals yield the input–output data necessary for the application of LTP system identification techniques, allowing us to estimate harmonic transfer functions (HTFs) associated with the local LTP approximation to the system dynamics around the limit cycle. We compare actual system responses with responses predicted by the HTF, providing evidence that data-driven system identification methods can be used to construct models for locomotor behaviours.Item Open Access Independent estimation of input and measurement delays for a hybrid vertical spring-mass-damper via harmonic transfer functions(IFAC, 2015-06) Uyanık, İsmail; Ankaralı, M. M.; Cowan, N. J.; Saranlı, U.; Morgül, Ömer; Özbay, HitaySystem identification of rhythmic locomotor systems is challenging due to the time-varying nature of their dynamics. Even though important aspects of these systems can be captured via explicit mechanics-based models, it is unclear how accurate such models can be while still being analytically tractable. An alternative approach for rhythmic locomotor systems is the use of data-driven system identification in the frequency domain via harmonic transfer functions (HTFs). To this end, the input-output dynamics of a locomotor behavior can be linearized around a stable limit cycle, yielding a linear, time-periodic system. However, few if any model-based or data-driven identification methods for time-periodic systems address the problem of input and measurement delays in the system. In this paper, we focus on data-driven system identification for a simple mechanical system and analyze its dynamics in the presence of input and measurement delays using HTFs. By exploiting the way input delays are modulated by the periodic dynamics, our results enable the separate, independent estimation of input and measurement delays, which would be indistinguishable were the system linear and time invariant. © 2015, IFAG.