Design and optimisation of regression-type small phase shift FIR filters and FIR-based differentiators with optimal local response in LS-sense

Saków, Mateusz; Marchelek, K.

doi:10.1016/j.ymssp.2020.107408

Cited by 9 publications

(11 citation statements)

References 59 publications

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“…In real objects, with unknown disturbances, the level of compensation will not provide such perfect control and greater influence of the PD control should be expected. Moreover, the simulation did not consider signal processing delays that occur in real conditions, therefore, analysis of the results of empirical studies is necessary [49][50][51][52][53].…”

Section: Analysis Of the Resultsmentioning

confidence: 99%

Modeling and Control of an Underactuated System for Dynamic Body Weight Support

2021

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This article concerns the stability analysis of a control system for a dynamic body weight support system in a rehabilitation device for the re-education of human gait. The paper presents a physical model of the device, which characterizes the most important physical phenomena associated with the movement of the system, i.e., inertia, damping, and elasticity. The device has one active and one passive element. They are connected by a connector with elastic and damping properties. This solution provides the kinematic chain required due to interactions with humans, while at the same time ensures that the device is an underactuated system. The article also presents the methodology used to verify the stability of the control system while acting as an active body weight support system. The paper formulates the mathematical model of the system that was used in the synthesis of control using the Lyapunov theory of stability. The results of simulation and experimental tests are also presented.

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Section: Analysis Of the Resultsmentioning

confidence: 99%

Modeling and Control of an Underactuated System for Dynamic Body Weight Support

2021

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“…Thereby, this proposal considers the implementation of a pre-processing stage that aims to filter a specific range of frequencies of interest, in which, the electromechanical system under study, may generate the fault-related frequency components. Specifically, the considered filter is a digital FIR-based low pass filter that is implemented in software, and it is designed under the window method [ 45 ]. The digital low pass filter based on the windowing method works the under basic principle of the ideal “brick wall” filter with a cut-off frequency of

following:

where, the low pass filter produces a magnitude equal to one for all frequencies that have frequency values less than

, and produces a magnitude equal to 0 for those frequency values between

and

.…”

Section: Methodsmentioning

confidence: 99%

Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems

Arellano-Espitia

Delgado-Prieto

Gonzalez-Abreu

et al. 2021

Sensors

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The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.

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“…In the case of the integer differentiation and integration, passive electrical components such as capacitors and coils can carry out these operations on the analogue signal [29]. Moreover, the digital representation of the real-time integer-order calculus is also trivial and addressed by existing solutions and scienti ic papers [4,5,[30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…Another design method is based on the closed-form Newton-Cotes integration formula [68], where the third-order digital trapezoidal integrator is obtained and inverted to form the differentiator. Another solution was proposed in [70], where the adaptation of the method of least squares was used to design the integer FIR differentiator. Furthermore, alternative methods are the Adomian decomposition method [71], the Chebyshev collocation method [72,73], and the predictor-corrector approach based on the Adams-Bashforth-Moulton method [74][75][76].…”

Section: Introductionmentioning

confidence: 99%

Design of Small-Phase Time-Variant Low-pass Digital Fractional Differentiators and Integrators

Sakow

2024

JAMRIS

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The design method and the time-variant FIR architecture for real-time estimation of fractional and integer differentials and integrals are presented in this paper. The proposed FIR architecture is divided into two parts. Small-phase filtering, integer differentiation, and fractional differential and integration on the local data are performed by the first part, which is time-invariant. The second part, which is time-variant, handles fractional and global differentiation and integration. The separation of the two parts is necessary because real-time matrix inversion or an extensive analytical solution, which can be computationally intensive for high-order FIR architectures, would be required by a single time-variant FIR architecture. However, matrix inversion is used in the design method to achieve negligible delay in the filtered, differentiated, and integrated signals. The optimum output obtained by the method of least squares results in the negligible delay. The experimental results show that fractional and integer differentiation and integration can be performed by the proposed solution, although the fractional differentiation and integration process is sensitive to the noise and limited resolution of the measurements. In systems that require closed-loop control, disturbance observation, and real-time identification of model parameters, this solution can be implemented.

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Design and optimisation of regression-type small phase shift FIR filters and FIR-based differentiators with optimal local response in LS-sense

Cited by 9 publications

References 59 publications

Modeling and Control of an Underactuated System for Dynamic Body Weight Support

Modeling and Control of an Underactuated System for Dynamic Body Weight Support

Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems

Design of Small-Phase Time-Variant Low-pass Digital Fractional Differentiators and Integrators

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