Application of the Welch-Method for the Identification of Two- and Three-Mass-Systems

Villwock, S.; Pacas, Mario

doi:10.1109/tie.2007.909753

Cited by 113 publications

(59 citation statements)

References 15 publications

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“…Moreover, in order to validate the rough online estimates of the anti-resonance f ares and resonance f res the experimental system is offline identified by considering the open-loop identification method proposed in (Villwock and Pacas, 2008). A PRBS is used to excite the system, and the experimental frequency response estimate G e (jω) is obtained by Welch method.…”

Section: Resultsmentioning

confidence: 99%

“…The spectral estimates can be obtained in different ways (Villwock and Pacas, 2008), but in general, Eq. (1) gives a good approximation of the real system G(e jω ) based on the assumption that the measurement noise n(k) and the input are uncorrelated, meaning that S un (e jω ) = 0.…”

Section: Problem Statementmentioning

confidence: 99%

“…This type of frequency response estimator Eq. (1) has been successfully used in the identification of a closed-loop controlled system by setting the controller bandwidth relatively low (Beineke et al, 1998) (Villwock and Pacas, 2008). However, when noise is affecting the system input u(k), the method can give poor frequency response estimations if a separate noise model estimation is not included in the estimation routine.…”

Section: Problem Statementmentioning

confidence: 99%

“…(19)- (21) is considered, where input u(k) and output y(k) signals are used in the identification process. Obviously, the inherent problem of direct identification schemes arises as a result of correlation between the input and output signals used in the identification experiment, but as the low bandwidth controller is considered the direct identification is applicable for frequency response estimation (Villwock and Pacas, 2008). It is also remarked that, in practical applications several noise and disturbances sources can be found which influences the frequency response estimation.…”

Section: Frequency Response Estimationmentioning

confidence: 99%

“…In the case of non-parametric identification, typically no (or few) assumptions are made with respect to the model structure. The wellestablished non-parametric frequency-domain identification methods (Heath, 2001), (Villwock and Pacas, 2008), (Schoukens et al, 2012) are non-recursive, and based on the availability of the whole data record required to perform the necessary calculations. This implies that the identification must, in practice, be performed using offline or batch data processing.…”

Section: Introductionmentioning

confidence: 99%

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Online Identification of a Two-Mass System in Frequency Domain using a Kalman Filter

Nevaranta

Derammelaere

Parkkinen

et al. 2016

MIC

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Some of the most widely recognized online parameter estimation techniques used in different servomechanism are the extended Kalman filter (EKF) and recursive least squares (RLS) methods. Without loss of generality, these methods are based on a prior knowledge of the model structure of the system to be identified, and thus, they can be regarded as parametric identification methods. This paper proposes an on-line non-parametric frequency response identification routine that is based on a fixed-coefficient Kalman filter, which is configured to perform like a Fourier transform. The approach exploits the knowledge of the excitation signal by updating the Kalman filter gains with the known time-varying frequency of chirp signal. The experimental results demonstrate the effectiveness of the proposed online identification method to estimate a non-parametric model of the closed loop controlled servomechanism in a selected band of frequencies.

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Section: Resultsmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Frequency Response Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online Identification of a Two-Mass System in Frequency Domain using a Kalman Filter

Nevaranta

Derammelaere

Parkkinen

et al. 2016

MIC

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Event‐triggered model reference adaptive control system design for SISO plants using meta‐learning‐based physics‐informed neural networks without labeled data and transfer learning

Duanyai,

Song,

Konghuayrob

et al. 2024

Adaptive Control & Signal

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SummaryThis paper examines the controllability of a novel Lyapunov‐based model reference adaptive control (MRAC) system designed with a meta‐learning‐based physics‐informed neural network (MLPINN) for linear and nonlinear single‐input and single‐output (SISO) plants without labeled data (MLPINN‐MRAC system). It is devised with the benefits of several techniques: the integration of the identification process in a training mode into an online control mode (straightforward design); no labeled data generation for online identification by a physics‐informed neural network; the prevention of degradation in tracking performance by meta‐learning, as the system triggers a meta‐learning process only when an error threshold detects the deterioration (high efficiency and the reduction of computation cost); quick adaptation to new inputs and an updated control input for each sub‐time span by transfer learning. It is worth noting that the frequency of the meta‐learning event detection significantly affects the step response stability of the nonlinear plant. To achieve a better quality of the stabilization, more frequent event detection is necessary for both beginning and end intervals in control. Sixteen triggering events are enough to shape the acceptable step response of the nonlinear plant, and 44 triggering events achieve the plant's desired step response with its minor modeling error. While, as for the linear plant, a single triggering event is sufficient to attain its tolerable step response and modeling error, implying that intensive event detection is not critical in the identification. It is obvious that the MLPINN‐MRAC system functions well and is more beneficial and efficient for the nonlinear plant.

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Automated detection of the yellow‐legged hornet (Vespa velutina) using an optical sensor with machine learning

Herrera

Williams²,

Encarnação³

et al. 2022

Pest Management Science

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BACKGROUND: The yellow-legged hornet (Vespa velutina) is native to Southeast Asia and is an invasive alien species of concern in many countries. More effective management of populations of V. velutina could be achieved through more widespread and intensive monitoring in the field, however current methods are labor intensive and costly. To address this issue, we have assessed the performance of an optical sensor combined with a machine learning model to classify V. velutina and native wasps/hornets and bees. Our aim is to use the results of the present work as a step towards the development of a monitoring solution for V. velutina in the field.RESULTS: We recorded a total 935 flights from three bee species: Apis mellifera, Bombus terrestris and Osmia bicornis; and four wasp/hornet species: Polistes dominula, Vespula germanica, Vespa crabro and V. velutina. The machine learning model achieved an average accuracy for species classification of 80.1 ± 13.9% and 74.5 ± 7.0% for V. velutina. V. crabro had the highest level of misclassification, confused mainly with V. velutina and P. dominula. These results were obtained using a 14-value peak and valley feature derived from the wingbeat power spectral density.CONCLUSION: This study demonstrates that the wingbeat recordings from a flying insect sensor can be used with machine learning methods to differentiate V. velutina from six other Hymenoptera species in the laboratory and this knowledge could be used to help develop a tool for use in integrated invasive alien species management programs.

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Application of the Welch-Method for the Identification of Two- and Three-Mass-Systems

Cited by 113 publications

References 15 publications

Online Identification of a Two-Mass System in Frequency Domain using a Kalman Filter

Online Identification of a Two-Mass System in Frequency Domain using a Kalman Filter

Event‐triggered model reference adaptive control system design for SISO plants using meta‐learning‐based physics‐informed neural networks without labeled data and transfer learning

Automated detection of the yellow‐legged hornet (Vespa velutina) using an optical sensor with machine learning

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