Abstract
In this work, a new method is proposed to identify systems in the form of first and second-order 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.
Highlights
The system identification plays a vital role in estimating appropriate models from input/output data for behavior prediction, simulation modeling, monitoring and controller synthesis [1], [2]
First is the PROCEST algorithm of MATLAB’s system identification toolbox, and second is the TFSRIVC algorithm of [12], which is available in the CONTSID toolbox [38]
This paper presented a simple, fast, robust, and accurate method to simultaneously identify all the parameters, including the time-delay of first order-CTD (FOCTD), second order-CTD (SOCTD), and SOCTD with zero (SOCTDZ) models
Summary
H Approximate Hessian matrix I Identity matrix J Jacobian matrix K System gain N Data length T Sample time Tr Transpose operator u Input variable V Objective function w Noise disturbance variable x Noise-free output variable y Noisy output variable
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