Parameter estimation of integrating and time delay processes using single relay feedback test

Panda, Rames C.; Vijayan, V.; Sujatha, V.; Pandey, Deepa; Manamali, D.; Mandal, Abhijit

doi:10.1016/j.isatra.2011.06.004

Cited by 42 publications

(25 citation statements)

References 16 publications

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“…To overcome this problem, consider a step input function of height applied at t = 0. The following integral holds for t ≥ L: 14 (9) For t ≥ L the estimation equation (7) can be written: (10) This can be expanded as follows: (11) Equation (11) can be expressed in least-squares estimation form as: (12) Using more general terminology, equation (12) can be written in the form (13) where ; ;…”

Section: Integral Equation (Ie) Methods Equation Developmentmentioning

confidence: 99%

“…10 An alternative approach is to use an auto-tuning procedure based on relay feedback. 11 More recently, a family of methods have been published that are more computationally involved, but allow the parameters of the transfer function model, including the time delay, to be estimated simultaneously. A common feature of these new methods is that the data used in the evaluations is obtained in discrete-time.…”

Section: Introductionmentioning

confidence: 99%

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A hands-on approach to teaching system identification using first-order plus dead time modelling of step response data

Burn

Cox

2018

The International Journal of Electrical Engineering & Educa

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This paper describes three step response-based system identification methods of increasing complexity, together with a range of exercises that will enhance student understanding of this area in an engaging and practical way. For illustration purposes and practicality, it is assumed that the model to be identified is of the first order plus dead time (FOPDT) type. The first method uses a popular graphical technique, which is easy to understand and apply, but inaccurate when the response data is not ideal. The second uses the Nelder-Mead simplex method, which is a more powerful technique and has the added benefit of introducing undergraduate students to the concepts of numerical optimisation. The third uses an integral equation (IE) algorithm. The latter two methods, which can be readily extended to other model structures and input types, are also demonstrated using experimental data obtained from a tank level control system.

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Section: Integral Equation (Ie) Methods Equation Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hands-on approach to teaching system identification using first-order plus dead time modelling of step response data

Burn

Cox

2018

The International Journal of Electrical Engineering & Educa

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“…Majhi [3] proposed relay-based identification of integrating process model parameters by state space technique. Recently Panda et al [4] proposed estimation of integrating and time delay processes using single relay feedback test. Liu and Gao [5] derived exact relay response expressions for integrating and unstable processes by using Newton-Raphson iteration method.…”

Section: Introductionmentioning

confidence: 99%

Smart Relay-Based Online Estimation of Process Model Parameters

Bajarangbali

Majhi

2014

Advances in Intelligent Systems and Computing

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This paper presents online estimation of unstable and integrating time delay process model parameters using a smart relay. The describing function (DF) approximation of relay not only results in simpler analytical expressions but also enables one to estimate the model parameters with significant accuracy. Measurement noise is an important issue during estimation of process model parameters. The smart relay is capable of emulating the dynamics of a conventional relay and also of rejecting the ill effects of measurement noise. Simulation results show the usefulness of the identification technique.

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“…However, this method successfully recovers the noise at peak amplitude whereas yields an inaccurate ultimate period. Panda et al [13], implemented single relay feedback test to estimate the process model parameters of integrating and time delay processes. Under noisy environment, authors utilized a relay with hysteresis for the mitigation of transient fluctuation in the relay output at zero crossings.…”

Section: Introductionmentioning

confidence: 99%

Identification of system dynamics in presence of measurement noise

Pandey

Majhi

2013

2013 Annual IEEE India Conference (INDICON)

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A technique for identification of system dynamics under the influence of measurement noise is presented. When output signal from sensor is corrupted by noise, identification of system parameters become an arduous task. Therefore, denoising methods can be employed in order to achieve a clean limit cycle under noisy environment. A Fast Fourier Transform (FFT) method has been reported as an effective technique for the mitigation of noise by an appropriate thresholding. Generalized explicit expressions are examined to obtain unknown plant model parameters for stable and unstable First Order Plus Dead Time (FOPDT) processes. Examples are illustrated to show the effectiveness of method during parametric identification of process models. Subsequently, a comparison is drawn between estimated and actual system through simulation by step test and Nyquist plot.

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Parameter estimation of integrating and time delay processes using single relay feedback test

Cited by 42 publications

References 16 publications

A hands-on approach to teaching system identification using first-order plus dead time modelling of step response data

A hands-on approach to teaching system identification using first-order plus dead time modelling of step response data

Smart Relay-Based Online Estimation of Process Model Parameters

Identification of system dynamics in presence of measurement noise

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