Relay feedback method for processes under noisy environments

Kim, Jin Su; Byeon, Jeonguk; Chun, Daewoong; Sung, Su Whan; Lee, Jietae; Edgar, Thomas F.

doi:10.1002/aic.11982

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…There are several chattering‐free methods for noisy environments2 and peaks in the process input can be removed because such methods do not need the low‐pass filter F

_{L}^{−1}

( s ) to smooth noise. For this, a dead‐zone relay whose dead‐zone is determined by the integral of the process output18 is considered for the identification of ultimate information without sharp peaks in the process input. The relay is switched as shown in Figure 5, where y r ( t ) is the filtered output and y ri ( t ) is its integral.…”

Section: Modified Dead‐zone Relay With Filters (Mdrf)mentioning

confidence: 99%

“…Let t 1 and t 2 be durations of relay outputs of zero and on(off), respectively. Then the process ultimate period is P u = p = 2( t 1 + t 2 ) and the process ultimate gain is18 …”

Section: Modified Dead‐zone Relay With Filters (Mdrf)mentioning

confidence: 99%

“…A low‐pass filter in the feedback loop may be used, but it also suffers from the same problem due to the phase lag of the filter. Recently dead‐zone relay methods to provide offset‐free ultimate data under noisy environments are investigated 18. For processes with slow drifts or biases, relay feedback oscillations are asymmetric and result in errors for ultimate data estimation.…”

Section: Introductionmentioning

confidence: 99%

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Relay feedback identification for processes under drift and noisy environments

et al. 2010

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in Wiley Online Library (wileyonlinelibrary.com).Relay feedback identification methods are widely used to find the process ultimate information and tune proportional-integral-derivative controllers. The conventional relay feedback method has several disadvantages, which include poor estimates of the process ultimate information for low-order processes, chattering of relay for noisy environments, and asymmetric relay responses for constant biases or slow drifts in the process outputs. Methods to mitigate each of the above disadvantages are available. However, a systematic method to treat all of them has not been studied yet. Here, simple relay feedback methods that resolve these problems by introducing band-pass filters in the feedback loop are proposed. The high-pass filter part in band-pass filter removes a constant bias or low frequency drift, and the low-pass filter part removes high frequency noise and high-order harmonic terms in the relay feedback oscillation, resulting better estimates of the process ultimate information. Because filters used for the proposed methods are able to reject constant biases, the process steady state gains can be estimated without disturbing the relay feedback oscillations and first order plus time delay (FOPTD) models can be obtained by combining the process steady state gains with the relay oscillation information. V V C

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“…There are several chattering‐free methods for noisy environments2 and peaks in the process input can be removed because such methods do not need the low‐pass filter F

_{L}^{−1}

Section: Modified Dead‐zone Relay With Filters (Mdrf)mentioning

confidence: 99%

“…Let t 1 and t 2 be durations of relay outputs of zero and on(off), respectively. Then the process ultimate period is P u = p = 2( t 1 + t 2 ) and the process ultimate gain is18 …”

Section: Modified Dead‐zone Relay With Filters (Mdrf)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Relay feedback identification for processes under drift and noisy environments

et al. 2010

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“…The Fourier series based curve fitting technique was used to recover the limit cycle by a non-linear least squares method and trust-region algorithm with resetting the relay amplitude to obtain half cycle data. Kim et al [12], utilized a simple method by concatenating an integrator at the process output for the minimization of multiple switching at the relay output, which ultimately increases overall order of process dynamics. This novel approach successfully mitigate the noise content in higher order dynamical processes but fails while identifying the lower order process models.…”

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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