Least squares and output error identification algorithms for continuous time systems with unknown time delay operating in open or closed loop

Baysse, Arnaud; Carrillo, Francisco Javier; Habbadi, Abdallah

doi:10.3182/20120711-3-be-2027.00279

Cited by 2 publications

(3 citation statements)

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“…To solve the linear regression problem in (7), the SRIVC algorithm uses an instrumental variable (IV) method. The instruments in the SRIVC method are chosen as the estimated noise free outputs, i.e.…”

Section: Traditional Srivc Methodsmentioning

confidence: 99%

“…Let L(p) be a CT low-pass filter with cut off frequency ω L c . Applying the filter L(p) to the linear regression (7), we obtain ȳ(n)…”

Section: Srivc-based Time Delay Estimation With Filteringmentioning

confidence: 99%

“…Another approach is to model the delay by a rational polynomial transfer function using a Padé or similar approximation and then estimate the time delay as part of the system parameters [1] [2] [18]. In [27] [6] [7], the time delay and system parameters of a Multiple Input Single Output CT system are estimated in a separable way using an iterative global nonlinear least-squares or instrumental variable method.…”

Section: Introductionmentioning

confidence: 99%

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Useful redundancy in parameter and time delay estimation for continuous-time models

Welsh

Alamir

2018

Automatica

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This paper proposes an algorithm to estimate the parameters, including time delay, of continuous time systems based on instrumental variable identification methods. To overcome the multiple local minima of the cost function associated with the estimation of a time delay system, we utilise the useful redundancy technique. Specifically, the cost function is filtered through a set of low-pass filters to improve convexity with the useful redundancy technique exploited to achieve convergence to the global minimum of the optimization problem. Numerical examples are presented to demonstrate the effectiveness of the proposed algorithm.

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Section: Traditional Srivc Methodsmentioning

confidence: 99%

“…Let L(p) be a CT low-pass filter with cut off frequency ω L c . Applying the filter L(p) to the linear regression (7), we obtain ȳ(n)…”

Section: Srivc-based Time Delay Estimation With Filteringmentioning

confidence: 99%