A Novel Iterative System Identification and Modeling Scheme With Simultaneous Time-Delay and Rational Parameter Estimation

Abstract

In this work, a new method is proposed to identify systems in the form of first and secondorder continuous time-delayed (CTD) models where time-delay and rational parameters are estimated simultaneously. The time delay is explicitly brought into system parameters by discretizing the CTD models. The sampled input-output data from the system is used in an iterative prediction error minimization (PEM) algorithm to identify the discrete-time delayed (DTD) model from which the equivalent CTD model is extracted. The DTD model includes the fractional and integer part of time-delay, where the phase contribution due to the fractional part is used to correct the estimation of delay as well as the termination of the algorithm until the phase contribution lies within one sample time. The efficacy of the proposed method is demonstrated by the simulation study with noisy measurements for four different systems along with the experimental validation.

An indirect approach for online identification of continuous time‐delay systems

“…4. Return CFOTD or CSOTD model parameters using ( 14), ( 15), ( 16) and (17) or using (31), (32), (33) and (17), respectively.…”

“…and the time-delay L is computed by (17) with substitution from (33), utilizing the same procedure as in case of the CFOTD system.…”

“…An another iterative indirect offline method is reported in Ref. 17 for identifying time‐delay and rational parameters simultaneously. Furthermore, the identified first‐ and second‐order delay dynamics in the continuous time‐domain has extensively used for model‐based controller design 18,19,20,21 …”

Vision-Based System Identification of a Quadrotor

“…Utilizing inputs and outputs like motor speeds, control inputs, and position measurements, these studies estimate high-fidelity models that represent the quadrotor's behavior. These studies have explored various approaches, including parameter estimation [8] [9], model structure selection [10], and nonlinear system identification methods [11], to capture the intricate dynamics of quadrotors. Most quadrotor modeling studies using system identification techniques have focused on advanced motion-tracking systems rather than vision-based approaches.…”