TY - JOUR
T1 - Independent estimation of input and measurement delays for a hybrid vertical spring-mass-damper via harmonic transfer functions
AU - Uyamk, Ismail
AU - Ankarali, M. Mert
AU - Cowan, Noah J.
AU - Saranli, Uluç
AU - Morgül, Ömer
AU - Özbay, Hitay
N1 - Funding Information:
★★ This material is based on work supported by the National Science ★ This material is based on work supported by the National Science FoTunhdisatmioanterGiarlanistsba0s8e4d5o7n49woarnkdsu12p3p0o4r9te3d(btyo tNhe. NJ.atCioonwaalnS)c.ieTnhcee This material is based on work supported by the N˙ational Science
Publisher Copyright:
© 2015, IFAG.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - System 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.
AB - System 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.
KW - Harmonic transfer functions
KW - Legged locomotion
KW - System identification
KW - Time-delay estimation
KW - Time-periodic systems
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U2 - 10.1016/j.ifacol.2015.09.394
DO - 10.1016/j.ifacol.2015.09.394
M3 - Conference article
AN - SCOPUS:84992482175
SN - 2405-8963
VL - 28
SP - 298
EP - 303
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
IS - 12
T2 - 12th IFAC Workshop on Time Delay Systems, TDS 2015
Y2 - 28 June 2015 through 30 June 2015
ER -