Journal articles: 'Self-tuning filter'

1

Cao, Songling, and R. Russell Rhinehart. "A self-tuning filter." Journal of Process Control 7, no. 2 (January 1997): 139–48. http://dx.doi.org/10.1016/s0959-1524(96)00024-8.

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Mitić, Darko, Goran Jovanović, Mile Stojčev, and Dragan Antić. "ON DESIGN OF SELF-TUNING ACTIVE FILTERS." Facta Universitatis, Series: Automatic Control and Robotics 19, no. 1 (July 28, 2020): 059. http://dx.doi.org/10.22190/fuacr2001059m.

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In this paper, we present one approach in design of self-tuning all-pass, band-pass, low-pass and notch filters based on phase control loops with voltage-controlled active components and analyze their stability as well. The main idea is to vary signal delay of the filter and in this way to achieve phase correction. The filter phase characteristics are tuned by varying the transconductance of the operational transconductance amplifier or capacitance of an MOS varicap element, which are the constituents of filters. This approach allows us to implement active filters with capacitance values of order of pF, making the complete filter circuit to be amenable for realization in CMOS technology. The phase control loops are characterized by good controllable delay over the full range of phase and frequency regulation, high stability, and short settling (locking) time. The proposed circuits are suitable for implementation as a basic building RF function block, used in phase and frequency regulation, frequency synthesis, clock generation recovery, filtering, selective amplifying etc.

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JOVANOVIĆ, GORAN, DARKO MITIĆ, MILE STOJČEV, and DRAGAN ANTIĆ. "SELF-TUNING BIQUAD BAND-PASS FILTER." Journal of Circuits, Systems and Computers 22, no. 03 (March 2013): 1350008. http://dx.doi.org/10.1142/s0218126613500084.

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One approach to design self-tuning gm-C biquad band-pass filter is considered in this paper. The phase control loop is introduced to force filter central frequency to be equal to input signal frequency what is achieved by adjusting the amplifier transconductance gm. Thanks to that, the filter is robust to parameter perturbations and it can be used as a selective amplifier. In the full tuning range, it has a constant maximum gain at central frequency as well as a constant bandwidth. The 0.25 μm SiGe BiCMOS technology was used during design and verification of the band-pass filter. The filter has 26 dB gain, quality factor Q = 20 and central frequency up to 150 MHz. Simulation results indicate that the total in-band noise is 59 μV rms , the output third intercept point OIP3 = 4.36 dB and the dynamic range is 35 dB. Maximal power consumption at 3 V power supply is 1.115 mW.

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Kladovščikov, Leonid, and Romualdas Navickas. "DESIGN AND INVESTIGATION OF RESISTOR MATRIX FOR ACTIVE ANALOG RC FILTERS." Mokslas - Lietuvos ateitis 12 (January 28, 2020): 1–7. http://dx.doi.org/10.3846/mla.2020.11419.

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Resistor matrixes are widely used in active RC filters as well as in self-tuning systems. Using self-tuning systems for active RC filters, it is possible to automatically tune various parameters of filter – cut-off frequency, gain and quality of filter. Most recent multiband transceivers employ higher order filters for fine bandpass filtering, thus number of passive components increases. In this work, a novel resistor matrix structure and design method is proposed. Proposed resistor matrix structure compensates both integrated circuit process variations and temperature change. Proposed resistor matrix is designed using 0.18 μm TSMC CMOS technology node and investigated using Cadence Virtuoso software. For most accurate comparison of different resistor matrices, all of them were designed in same technology node using design techniques described in other authors’ works.

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Liu, Wen Qiang, Gui Li Tao, and Na Han. "Self-Tuning Weighted Fusion Kalman Filter for ARMA Signals." Applied Mechanics and Materials 538 (April 2014): 439–42. http://dx.doi.org/10.4028/www.scientific.net/amm.538.439.

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For the multisensor single channel autoregressive moving average (ARMA) signal with a white measurement noise and autoregressive (AR) colored measurement noises as common disturbance noises, when model parameters and noise statistics are partially unknown, a self-tuning weighted fusion Kalman filter is presented based on classical Kalman filter method. The local estimates are obtained by applying the recursive instrumental variable (RIV) and correlation method. Then the optimal weighted fusion Kalman filter is obtained by substituting all the fusion estimates into the corresponding optimal Kalman filter. A simulation example shows its effectiveness.

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Faiza, Aouiouat A., Sebaa Morsli, and Allaoui Tayeb. "Self Tuning Filter Based Fuzzy Logic Controller for Active Power Filter." Journal Européen des Systèmes Automatisés 53, no. 5 (November 15, 2020): 739–45. http://dx.doi.org/10.18280/jesa.530517.

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The performance of the Active Power Filter (APF) depends on the identification strategy of the reference currents and their control. Among the control strategies proposed in the literature to identifying the reference currents, that based on Self Tuning Filter (STF) which allows extracting directly the voltage and current fundamental components in the α-β axis at high performances, without any Phase Locked Loop (PLL). The performance of STF is function of its proportional parameter. However, there is no technique existed in the literature to dimensioning the proportional parameter of the STF filter. This paper presents an improved method for identifying the reference harmonic currents to be generated by the APF, based on STF for the extraction of harmonic currents and equipped with a Fuzzy Logic Corrector (FLC) in order to improve the waveform of the electric lines currents. The FLC adjust in real time the proportional parameter of the STF filter. The proposed FLC-STF regulator allows to having an optimal extraction of the harmonic currents. The conventional hysteresis method was used for the current control technique to generate the switching sequences of the static switches of the APF voltage inverter. The obtained simulation results, performed under the MATLAB/Simulink® environment on a system feeding a non-linear load, show good performance.

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Liu, Wen Qiang, Na Han, Man Yan, and Gui Li Tao. "Self-Tuning Fusion Kalman Filter for ARMA Signals." Applied Mechanics and Materials 229-231 (November 2012): 1768–71. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1768.

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For the single-channel autoregressive moving average (ARMA) signals with multisensor, and with unknown model parameters and noise variances, the local estimators of unknown model parameters and noise variances are obtained by the recursive instrumental variable (RIV) algorithm and correlation method, and the fused estimators are obtained by taking the average of the local estimators. Substituting them into the optimal fusion Kalman filter, a self-tuning fusion Kalman filter for single-channel ARMA signals is presented. A simulation example shows its effectiveness.

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Liu, Wen Qiang, Gui Li Tao, Ze Yuan Gu, and Song Li. "Self-Tuning Weighted Measurement Fusion Kalman Signal Filter." Applied Mechanics and Materials 274 (January 2013): 579–82. http://dx.doi.org/10.4028/www.scientific.net/amm.274.579.

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For the single channel autoregressive moving average (ARMA) signals with multisensor and a colored measurement noise, when the model parameters and noise variances are partially unknown, based on identification method and Gevers-Wouters algorithm with a dead band, a self-tuning weighted measurement fusion Kalman signal filter is presented. A simulation example applied to signal processing shows its effectiveness.

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9

MOIR, T. J., T. G. VISHWANATH, and D. R. CAMPBELL. "Real-time self-tuning deconvolution filter and smoother." International Journal of Control 45, no. 3 (March 1987): 969–85. http://dx.doi.org/10.1080/00207178708933781.

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Tao, Guili, and Zili Deng. "Self-Tuning Fusion Wiener Filter and Its Convergence." Procedia Engineering 29 (2012): 643–49. http://dx.doi.org/10.1016/j.proeng.2012.01.017.

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11

Horowitz, Roberto, Bo Li, and James McCormick. "Wiener Filter Based Minimum Variance Self-Tuning Regulation." IFAC Proceedings Volumes 29, no. 1 (June 1996): 5174–79. http://dx.doi.org/10.1016/s1474-6670(17)58502-x.

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HOROWITZ, R., B. LI, and J. W. McCORMICK. "Wiener-filter-based Minimum Variance Self-tuning Regulation." Automatica 34, no. 5 (May 1998): 531–45. http://dx.doi.org/10.1016/s0005-1098(97)00190-8.

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Yuan Gao, Wen-Jing Jia, Xiao-Jun Sun, and Zi-Li Deng. "Self-Tuning Multisensor Weighted Measurement Fusion Kalman Filter." IEEE Transactions on Aerospace and Electronic Systems 45, no. 1 (January 2009): 179–91. http://dx.doi.org/10.1109/taes.2009.4805272.

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14

Chung, Y. L., and S. A. Spiewak. "A Model of High Performance Dynamometer." Journal of Engineering for Industry 116, no. 3 (August 1, 1994): 279–88. http://dx.doi.org/10.1115/1.2901943.

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A narrow frequency bandwidth, strong fluctuations of the gain versus signal frequency and sensitivity to disturbances caused by the operating environments are the most common factors limiting the applicability of sensors in manufacturing systems. Self-tuning filters represent an efficient means of alleviating these limitations. Since the dynamic properties of sensors vary rapidly, a successful implementation of sensors coupled with self-tuning filters hinges upon accurate, real-time adjustments of these filters. The selection of optimum filter settings, based upon the available distorted output signals from the in-process sensors, poses a difficult problem. In general, the algorithm of self tuning requires a priori information about the sensor and its environment, condensed into a form of an analytical model. A systematic approach to the analytical modeling of sensors is proposed. To illustrate this approach, a comprehensive model of a commercial dynamometer is developed and tested.

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Jovanović, Goran, Darko Mitić, Mile Stojčev, and Dragan Antić. "Self-Tuning OTA-C Notch Filter with Constant Q-Factor." Journal of Circuits, Systems and Computers 25, no. 05 (February 25, 2016): 1650045. http://dx.doi.org/10.1142/s0218126616500456.

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This paper describes the design of a self-tuning OTA-C notch filter (NF), based on a phase control loop concept. The phases of filter input and output signals are compared and the phase error is used to generate the control voltage, which adjusts the transconductances of operational transconductance amplifiers (OTAs) and, consequently, tunes the NF center frequency without quality factor variation. The proposed solution is robust to filter parameter variations. The filter is implemented in 0.13[Formula: see text][Formula: see text]m SiGe BiCMOS technology. Under the typical circuit operating conditions, the center frequency tuning is within the range from 70[Formula: see text]MHz up to 150[Formula: see text]MHz, with tuning error less than 1%. The power consumption is less than 3.6[Formula: see text]mW, the 1[Formula: see text]dB gain compression point is [Formula: see text]2.5[Formula: see text]dBm, the input (IIP3) and output (OIP3) intercept points are 3.47 and 9.07[Formula: see text]dBm, respectively, and the total filter effective noise voltage is 4.35[Formula: see text][Formula: see text]Vrms. The filter structure is suitable for complementary metal-oxide-semiconductor (CMOS) implementation.

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Tao, Gui Li, and Zi Li Deng. "The Self-Tuning Distributed Information Fusion Kalman Filter for ARMA Signals." Applied Mechanics and Materials 48-49 (February 2011): 1305–9. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.1305.

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For the multisensor Autoregressive Moving Average (ARMA) signals with unknown model parameters and noise variances, using the Recursive Instrumental Variable (RIV) algorithm, the correlation method and the Gevers-Wouters algorithm with dead band, the fused estimators of unknown model parameters and noise variances can be obtained. Then substituting them into optimal fusion signal filter weighted by scalars, a self-tuning distributed fusion Kalman filter is presented. Using the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning fused Kalman signal filter converges to the optimal fused Kalman signal filter, so that it has asymptotic optimality. A simulation example shows its effectiveness.

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17

Peters, S. D., and A. Antoniou. "A self-tuning NLMS adaptive filter using parallel adaptation." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 44, no. 1 (1997): 11–21. http://dx.doi.org/10.1109/82.559365.

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Liao, Tianrui, Kaoru Hirota, Xiangdong Wu, Shuai Shao, and Yaping Dai. "A Dynamic Self-Tuning Maximum Correntropy Kalman Filter for Wireless Sensors Networks Positioning Systems." Remote Sensing 14, no. 17 (September 1, 2022): 4345. http://dx.doi.org/10.3390/rs14174345.

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To improve the accuracy of the maximum correntropy Kalman filter (MCKF) in wireless sensors networks (WSNs) positioning, a dynamic self-tuning maximum correntropy Kalman filter (DSTMCKF) is proposed, where innovation and the sensors information of the WSNs are used to adjust the noise covariance matrices, and the maximum correntropy criterion is the criterion for the filter’s optimality. By dynamically adjusting the noise covariance matrices, the DSTMCKF ensures that the correntropy distribution is accurate in the presence of non-Gaussian noise (NGN), thus improving its ability to handle the NGN. In simulation and real environment positioning experiments, the DSTMCKF is used to compare with the MCKF, variable kernel width–maximum correntropy Kalman filter (VKW-MCKF) and robust minimum error entropy Kalman filter (R-MEEKF). Among the four filters, the DSTMCKF has the highest accuracy, and the error of the DSTMCKF is reduced by 34.5, 42.9 and 40.0%, respectively, compared with the MCKF, VKW-MCKF and R-MEEKF in the real-world environment positioning experiment. The application of the DSTMCKF in WSNs positioning systems improves the stability of the control systems because of the rising positioning accuracy, which makes WSNs positioning systems more widely used in scenarios requiring high stability, such as automatic parking.

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Guo, Xing Long, Z. L. Wang, J. Huang, Z. J. Zhang, H. H. Yin, and H. F. Zhu. "Tunable Low-Pass MEMS Filter Using Defected Ground Structures (DGS)." Advanced Materials Research 712-715 (June 2013): 1798–801. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.1798.

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In this paper, fully monolithic tunable millimeter-wave filters using defected ground structures (DGS) are proposed using the CPW-based periodic structures with novel multiple-contact MEMS switches. Millimeter-wave low-pass filters were designed, fabricated, and tested. The cascaded CPW-based periodic structures, with low-pass intrinsic filtering characteristics, are reconfigured into a self-similar single unit cell by the operation of the novel multiple-contact MEMS switches with single actuation. In the first order tuning, the 3-dB cut-off frequency changes from 8.2GHz to10.5GHz, and the second order tuning is 8.2GHz to 16.8GHz. The tested results show that the pass-band ripple is less than 1.2dB and the maximal out-of-band rejection is better than 27dB. The chip size of the low-pass filter is 2.5mm×1.2mm.

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Zhang, Yong, and Xu-Feng Cheng. "Sensorless Control of Permanent Magnet Synchronous Motors and EKF Parameter Tuning Research." Mathematical Problems in Engineering 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/3916231.

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This paper concerns the parameter tuning and the estimated results postprocessing of the extended Kalman filter for the sensorless control application of permanent magnet synchronous motors. At first an extended Kalman filter parameter tuning method is proposed based on the theoretical and simulation analysis of extended Kalman filter parameters. Furthermore, a sensorless control system is proposed based on the parameter tuning method and the simulation analysis of extended Kalman filter estimation results in different reference speeds and different load torques. The proposed sensorless control system consists of two parts. The first one is a module to self-regulate extended Kalman filter parameters. The second part can correct the estimated speed and the estimated rotation angle based on the reference speed and the electromagnetic torque. Finally, simulation results are presented to verify the feasibility and validity of the proposed sensorless control system.

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Wang, Xin, and Shu-Li Sun. "Measurement Feedback Self-Tuning Weighted Measurement Fusion Kalman Filter for Systems with Correlated Noises." Journal of Applied Mathematics 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/324296.

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For the linear discrete stochastic systems with multiple sensors and unknown noise statistics, an online estimators of the noise variances and cross-covariances are designed by using measurement feedback, full-rank decomposition, and weighted least squares theory. Further, a self-tuning weighted measurement fusion Kalman filter is presented. The Fadeeva formula is used to establish ARMA innovation model with unknown noise statistics. The sampling correlated function of the stationary and reversible ARMA innovation model is used to identify the noise statistics. It is proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion Kalman filter, which means its asymptotic global optimality. The simulation result of radar-tracking system shows the effectiveness of the presented algorithm.

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Wang, Wen-Yu, and An-Wen Shen. "Detection and Reduction of Middle-Frequency Resonance for Industrial Servo with Self-Tuning Lowpass Filter." Journal of Control Science and Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/478907.

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A novel method for middle frequency resonance detection and reduction is proposed for speed control in industrial servo systems. Defects of traditional resonance reduction method based on adaptive notch filter in middle frequency range are analyzed. And the main reason is summarized as the difference between the resonance frequency and the oscillation frequency. A self-tuning low-pass filter is introduced in the speed feedback path, whose corner frequency is determined by FFT results and several self-tuning rules. With the proposed method the effective range of the adaptive filter is extended across the middle frequency range. Simulation and Experiment results show that the frequency detection is accurate and resonances during the speed steady states and dynamics are successfully reduced.

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Hajiyev, Ch M., and F. Caliskan. "Self-Tuning Kalman Filter Design for Offshore Platform Coordinates Estimation." IFAC Proceedings Volumes 34, no. 3 (May 2001): 177–82. http://dx.doi.org/10.1016/s1474-6670(17)34347-1.

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Berger, C. S. "Self-tuning control of offset using a moving average filter." IEE Proceedings D Control Theory and Applications 133, no. 4 (1986): 184. http://dx.doi.org/10.1049/ip-d.1986.0029.

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Lv, Wenjun, Yu Kang, and Yunbo Zhao. "Self-tuning asynchronous filter for linear Gaussian system and applications." IEEE/CAA Journal of Automatica Sinica 5, no. 6 (November 2018): 1054–61. http://dx.doi.org/10.1109/jas.2018.7511183.

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Gao, Yuan, Chenjian Ran, and Zili Deng. "Self-tuning measurement fusion Kalman filter with correlated measurement noises." Journal of Electronics (China) 26, no. 5 (September 2009): 614–22. http://dx.doi.org/10.1007/s11767-008-0036-5.

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Ran, Chenjian, and Zili Deng. "Self-tuning weighted measurement fusion Kalman filter and its convergence." Journal of Control Theory and Applications 8, no. 4 (November 2010): 435–40. http://dx.doi.org/10.1007/s11768-010-8243-7.

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TAO, Gui-Li, and Zi-Li DENG. "Self-tuning Fusion Kalman Filter with Unknown Parameters and Its Convergence." Acta Automatica Sinica 38, no. 1 (December 14, 2012): 109–19. http://dx.doi.org/10.3724/sp.j.1004.2012.00109.

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Dou, Yinfeng, Shuli Sun, and Chenjian Ran. "Self-tuning full-order WMF Kalman filter for multisensor descriptor systems." IET Control Theory & Applications 11, no. 3 (February 3, 2017): 359–68. http://dx.doi.org/10.1049/iet-cta.2016.0803.

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Sun, Xiaojun, Peng Zhang, and Zili Deng. "Self-tuning decoupled fusion Kalman filter based on the Riccati equation." Frontiers of Electrical and Electronic Engineering in China 3, no. 4 (August 26, 2008): 459–64. http://dx.doi.org/10.1007/s11460-008-0077-4.

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Djendaoui, Dahmane, Amar Benaissa, Boualaga Rabhi, and Laid Zellouma. "Self tunning filter for three levels four legs shunt active power filter with fuzzy logic controller." Acta Polytechnica 61, no. 3 (June 30, 2021): 415–27. http://dx.doi.org/10.14311/ap.2021.61.0415.

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The low harmonic distortion and reduced switching losses are the advantages of using the multilevel inverter. For this purpose, the three-level inverter is used in this paper as a three-phase four-leg shunt active power filter (SAPF). The SAPF is used to eliminate the harmonic current to compensate the reactive power current, and to balance the load currents under an unbalanced non-linear load. A fuzzy logic controller and self-tuning filters (STF) are used to control the active power filter (APF) and generate the reference current. To demonstrate the validity of the proposed control strategy, we compared it with a conventional p − q theory, under distortion voltage conditions and unbalanced non-linear load. The Matlab-Simulink toolbox is used to implement the algorithm of Fuzzy logic control. The performance of the SAPF controller is found very effective and adequate as compared with the p − q theory.

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Koganti, Srilakshmi, Krishna Jyothi Koganti, and Surender Reddy Salkuti. "Design of Multi-Objective-Based Artificial Intelligence Controller for Wind/Battery-Connected Shunt Active Power Filter." Algorithms 15, no. 8 (July 25, 2022): 256. http://dx.doi.org/10.3390/a15080256.

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Nowadays, the integration of renewable energy sources such as solar, wind, etc. into the grid is recommended to reduce losses and meet demands. The application of power electronics devices (PED) to control non-linear, unbalanced loads leads to power quality (PQ) issues. This work presents a hybrid controller for the self-tuning filter (STF)-based Shunt active power filter (SHAPF), integrated with a wind power generation system (WPGS) and a battery storage system (BS). The SHAPF comprises a three-phase voltage source inverter, coupled via a DC-Link. The proposed neuro-fuzzy inference hybrid controller (NFIHC) utilizes both the properties of Fuzzy Logic (FL) and artificial neural network (ANN) controllers and maintains constant DC-Link voltage. The phase synchronization was generated by a self-tuning filter (STF) for the effective working of SHAPF during unbalanced and distorted supply voltages. In addition, STF also does the work of low-pass filters (LPFs) and HPFs (high-pass filters) for splitting the Fundamental component (FC) and Harmonic component (HC) of the current. The control of SHAPF works on d-q theory with the advantage of eliminating low-pass filters (LPFs) and phase-locked loop (PLL). The prime objective of the projected work is to regulate the DC-Link voltage during wind uncertainties and load variations, and minimize the total harmonic distortion (THD) in the current waveforms, thereby improving the power factor (PF).Test studies with various combinations of balanced/unbalanced loads, wind velocity variations, and supply voltage were used to evaluate the suggested method’s superior performance. In addition, the comparative analysis was carried out with those of the existing controllers such as conventional proportional-integral (PI), ANN, and FL.

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Chedjara, Zakaria, Ahmed Massoum, Patrice Wira, Ahmed Safa, and Abdelmadjid Gouichiche. "A New Quasi Open Loop Synchronization Technique for Grid-Connected Applications." Electrical, Control and Communication Engineering 17, no. 1 (June 1, 2021): 47–58. http://dx.doi.org/10.2478/ecce-2021-0006.

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Abstract This paper presents an effective quasi open-loop (QOLS) synchronization technique for grid -connected power converters that is organized in two different blocks. The first block is a new flexible technique for extracting the positive and negative sequence voltage under unbalanced and distorted conditions. It is a decoupled double self-tuning filter (DD-STF) or multiple self-tuning filters (M-STF) according to the conditions. The main advantages of this technique are its simple structure and the fact of being able to work under highly distorted conditions. Each harmonic is separately treated and this allows for selective compensation in active filter applications. The second block is the frequency detector; we propose a neural approach based on an ADALINE for online adaptation of the cut-off frequency of the DD-STF and M-STF considering a possible variation in the main frequency. The main advantage of this method is its immunity to the voltage signal amplitude and phase. In order to improve the performance of the frequency estimation under distorted source voltage, a pre-filtering stage is introduced. Experimental tests validate the proposed method and illustrate all its interesting features. Results show high performance and robustness of the method under low voltage ride through.

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Dewasthale, Mugdha, and R. D. Kharadkar. "High performance self tuning adaptive filter algorithm for noise cancellation in speech." Journal of Intelligent & Fuzzy Systems 32, no. 4 (March 29, 2017): 3167–76. http://dx.doi.org/10.3233/jifs-169260.

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Pascucci, D., M. Rubega, and G. Plomp. "Modeling time-varying brain networks with a self-tuning optimized Kalman filter." PLOS Computational Biology 16, no. 8 (August 17, 2020): e1007566. http://dx.doi.org/10.1371/journal.pcbi.1007566.

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Alraho, Senan, Qummar Zaman, and Andreas König. "Wide Programmable Range Fourth-Order, Fully-Differential Sallen-Key MOSFET-C LPF for Impedance Spectroscopy Measurements and Self-X Sensory Electronics in Industry 4.0." tm - Technisches Messen 88, s1 (August 24, 2021): s77—s82. http://dx.doi.org/10.1515/teme-2021-0064.

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Abstract This paper picks up the need for a wide range programmable corner frequency for anti-aliasing and antiimaging filters in on-chip impedance spectroscopy and sensor signal readout circuitry with self-X properties (selfdiagnosing/healing) for industry 4.0 applications. A fourthorder wide tunable range MOSFET-C low pass filter is designed by using XFAB 0.35 μm CMOS technology and Cadence design tools. The proposed circuit is based on fully differential Sallen-Key architecture with Butterworth approximation. It covers a frequency range from 30 Hz up to 7 MHz. Tunability is achieved using a potentially high resistance and linearized configurable MOS resistor to control the filter pole frequency. The configurable elements in the circuit serve as tuning knobs to be controlled by machine learning. The physical design area is 0.39mm2.

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Kovač, Uroš, and Andrej Košir. "Fast Estimatio of the Non-Stationary Amplitude of a Harmonically Distorted Signal Using a Kalman Filter." Metrology and Measurement Systems 20, no. 1 (March 1, 2013): 27–42. http://dx.doi.org/10.2478/mms-2013-0003.

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Abstract In this paper we introduce a self-tuning Kalman filter for fast time-domain amplitude estimation of noisy harmonic signals with non-stationary amplitude and harmonic distortion, which is the problem of a contactvoltage measurement to which we apply the proposed method. The research method is based on the self-tuning of the Kalman filter's dropping-off behavior. The optimal performance (in terms of accuracy and fast response) is achieved by detecting the jump of the amplitude based on statistical tests of the innovation vector of the Kalman filter and reacting to this jump by adjusting the values of the covariance matrix of the state vector. The method's optimal configuration of the parameters was chosen using a statistical power analysis. Experimental results show that the proposed method outperforms competing methods in terms of speed and accuracy of the jump detection and amplitude estimation.

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Wang, Chu-Tong, Jason S. H. Tsai, Chia-Wei Chen, You Lin, Shu-Mei Guo, and Leang-San Shieh. "An Active Fault-Tolerant PWM Tracker for Unknown Nonlinear Stochastic Hybrid Systems: NARMAX Model and OKID-Based State-Space Self-Tuning Control." Journal of Control Science and Engineering 2010 (2010): 1–27. http://dx.doi.org/10.1155/2010/217515.

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An active fault-tolerant pulse-width-modulated tracker using the nonlinear autoregressive moving average with exogenous inputs model-based state-space self-tuning control is proposed for continuous-time multivariable nonlinear stochastic systems with unknown system parameters, plant noises, measurement noises, and inaccessible system states. Through observer/Kalman filter identification method, a good initial guess of the unknown parameters of the chosen model is obtained so as to reduce the identification process time and enhance the system performances. Besides, by modifying the conventional self-tuning control, a fault-tolerant control scheme is also developed. For the detection of fault occurrence, a quantitative criterion is exploited by comparing the innovation process errors estimated by the Kalman filter estimation algorithm. In addition, the weighting matrix resetting technique is presented by adjusting and resetting the covariance matrix of parameter estimates to improve the parameter estimation for faulty system recovery. The technique can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection.

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Panigrahi, T. K., P. K. Dash, and P. K. Hota. "A self-tuning optimised unscented Kalman filter for voltage flicker and harmonic estimation." International Journal of Power and Energy Conversion 2, no. 3 (2010): 250. http://dx.doi.org/10.1504/ijpec.2010.037631.

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Mehdi, D., C. Humbert, C. Fonteix, J. Boudrant, and I. Mark. "Numerical Filter and PID Self Tuning Controller Using Estimation Constraints, Yeast Production Process." IFAC Proceedings Volumes 22, no. 6 (July 1989): 501–6. http://dx.doi.org/10.1016/s1474-6670(17)54427-4.

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Gao, Yang, Dapeng Tian, and Wang. "Fuzzy Self-tuning Tracking Differentiator for Motion Measurement Sensors and Application in Wide-Bandwidth High-accuracy Servo Control." Sensors 20, no. 3 (February 10, 2020): 948. http://dx.doi.org/10.3390/s20030948.

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Sensor differential signals are widely used in many systems. The tracking differentiator (TD) is an effective method to obtain signal differentials. Differential calculation is noise-sensitive. There is the characteristics of low-pass filter (LPF) in the TD to suppress the noise, but phase lag is introduced. For LPF, fixed filtering parameters cannot achieve both noise suppression and phase compensation lag compensation. We propose a fuzzy self-tuning tracking differentiator (FSTD) capable of adaptively adjusting parameters, which uses the frequency information of the signal to achieve a trade-off between the phase lag and noise suppression capabilities. Based on the frequency information, the parameters of TD are self-tuning by a fuzzy method, which makes self-tuning designs more flexible. Simulations and experiments using motion measurement sensors show that the proposed method has good filtering performance for low-frequency signals and improves tracking ability for high-frequency signals compared to fixed-parameter differentiator.

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Sohn, Sang-Wook, Young-Bin Lim, Jae-Jun Yun, Hun Choi, and Hyeon-Deok Bae. "A Filter Bank and a Self-Tuning Adaptive Filter for the Harmonic and Interharmonic Estimation in Power Signals." IEEE Transactions on Instrumentation and Measurement 61, no. 1 (January 2012): 64–73. http://dx.doi.org/10.1109/tim.2011.2150610.

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Shaik Nagul Sharif and Sri Latha Veerla. "Intelligent Hybrid-Fuzzy Controller using VLLMS Based Shunt Active Filter." International Journal for Modern Trends in Science and Technology 06, no. 09 (November 25, 2020): 215–29. http://dx.doi.org/10.46501/ijmtst060933.

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The power quality problem in the power system is increased with the use of non-linear devices. Due to the use of non-linear devices like power electronic converters, there is an increase in harmonic content in the source current. Due to this there is an increase in the losses, instability and poor voltage waveform. To mitigate the harmonics and provide the reactive power compensation, we use filters. There are different filters used in the power system. Passive filters provide limited compensation, so active filters can be used for variable compensation. In this work, a shunt active filter has been made adaptive using a Variable Leaky Least Mean Square (VLLMS) based controller. Proposed adaptive controller can be able to compensate for harmonic currents, power factor and nonlinear load unbalance. DC capacitor voltage has been regulated at a desired level using a PI controller and a self-charging circuit technique. But, this scheme as two disadvantages such as, tuning issues of current controller pre-requisites the traditional PI controller, which is controlled by intelligent based Hybrid-Fuzzy-Logic controller for achieving good performance features. The design concept of proposed intelligent Hybrid-Fuzzy controller for shunt active filter has been verified through simulation analysis and results are presented with proper comparisons.

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Tao, G. L., and Z. L. Deng. "Self-tuning fusion Kalman filter for multisensor single-channel ARMA signals with coloured noises." IMA Journal of Mathematical Control and Information 32, no. 1 (August 22, 2013): 55–74. http://dx.doi.org/10.1093/imamci/dnt027.

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Liu, Yu-Hsun, Nan-Chyuan Tsai, and Hsin-Lin Chiu. "Construction of Rotor/AMB dynamic model applied for TMP using self-tuning Kalman Filter." Mechatronics 24, no. 2 (March 2014): 158–67. http://dx.doi.org/10.1016/j.mechatronics.2014.01.002.

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Munakata, Toyonori, Takahiro Hada, and Michihito Ueda. "Self-tuning and stochastic resonance in a simple threshold system—a filter theory approach." Physica A: Statistical Mechanics and its Applications 375, no. 2 (March 2007): 492–98. http://dx.doi.org/10.1016/j.physa.2006.09.024.

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XU, NING-SHOU, and TAO SHOU. "Optimal moving average filter with application to self-tuning type control against deterministic disturbances." International Journal of Systems Science 22, no. 8 (August 1991): 1449–66. http://dx.doi.org/10.1080/00207729108910720.

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Jo, Jun-Gi, and Changsik Yoo. "Low-voltage and high-frequency Gm-opamp-C filter with automatic self frequency tuning." Analog Integrated Circuits and Signal Processing 50, no. 3 (January 25, 2007): 285–90. http://dx.doi.org/10.1007/s10470-007-9025-0.

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Banginwar, Pruthvi, and Timothy Sands. "Autonomous Vehicle Control Comparison." Vehicles 4, no. 4 (October 10, 2022): 1109–21. http://dx.doi.org/10.3390/vehicles4040059.

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Self-driving features rely upon autonomous control of vehicle kinetics, and this manuscript compares several disparate approaches to control predominant kinetics. Classical control using feedback of state position and velocities, open-loop optimal control, real-time optimal control, double-integrator patching filters with and without gain-tuning, and control law inversion patching filters accompanying velocity control are assessed in Simulink, and their performances are compared. Optimal controls are found via Pontryagin’s method of optimization utilizing three necessary conditions: Hamiltonian minimization, adjoint equations, and terminal transversality of the endpoint Lagrangian. It is found that real-time optimal control and control-law patching filter with velocity control incorporating optimization are the two best methods overall as judged in Monte Carlo analysis by means and standard deviations of position and rate errors and cost.

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Jayasankar, V. N., and U. Vinatha. "Backstepping Controller With Dual Self-Tuning Filter for Single-Phase Shunt Active Power Filters Under Distorted Grid Voltage Condition." IEEE Transactions on Industry Applications 56, no. 6 (November 2020): 7176–84. http://dx.doi.org/10.1109/tia.2020.3025520.

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Journal articles on the topic 'Self-tuning filter'

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