Application of Transient Slope of Zero and Pole in Bode Diagram in Automatic Identification of Filter Parameters

Journal of Control, Automation and Electrical Systems - Tập 33 - Trang 1840-1850 - 2022
Shiliang Zhao1, Jianxin Liu1, Yang Tan1,2, Kun Qian2
1School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, China
2School of Medical Technology, Beijing Institute of Technology, Beijing, China

Tóm tắt

Filters have a wide range of applications in many fields. Engineers generally use signal sources to manually test the characteristics of filters under test. In this research, we propose a system that automatically recognizes the characteristics and parameters of the analog filter throughout the entire process. This paper presents the theoretical conclusion that the instantaneous slope contribution at the n-order poles or zeros point in the Bode plot is $$\pm 10n$$  dB/dec (where ’+’ represents zeros, and ’−’ represents poles). At the same time, we propose a voltage-controlled adaptive frequency sweep method. The designed automatic test system is based on DDS (Direct Digital Synthesizer), DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array) and ADC (Analog-to-digital converter) to build the hardware platform. According to the sinusoidal excitation method, the original data of the voltage response is obtained by voltage-controlled adaptive frequency sweep and frequency re-sweep of the filter under test with DSP. The information of these special values approximately 10m dB/dec (m is an integer) in the original data is captured by our conclusion of the zero-pole transient slope. The cut-off frequency and bandwidth of filters to be tested are more accurately measured by the local linear method. Combined with the knowledge of fuzzy recognition rule and normalization, an automatic recognition algorithm is designed in DSP according to data classification rules, screening rules and normalization rules. Finally, the system automatically recognizes the types, orders and approximate position of filters’ poles and zeros of and the type of filters to be tested.

Tài liệu tham khảo

Alonso, C. H., Jazebi, S., & de Leon, F. (2016). Experimental parameter determination and laboratory verification of the inverse hysteresis model for single-phase toroidal transformers. IEEE Transactions on Magnetics, 52(11), 1–9. Barbosa, T. S., Ferreira, D. D., Pereira, D. A., Magalhães, R. R., & Barbosa, B. H. G. (2016). Fault detection and classification in cantilever beams through vibration signal analysis and higher-order statistics. Journal of Control, Automation and Electrical Systems, 27(5), 535–541. Basu, D. N. (2019). Dynamic frequency response of a single-phase natural circulation loop under an imposed sinusoidal excitation. Annals of Nuclear Energy, 132, 603–614. Chen, Z. M., Liu, Y., Liang, X., et al. (2019). A high efficiency band-pass filter based on CPW and quasi-spoof surface plasmon polaritons. IEEE Access, 8, 4311–4317. Fung, T. C. (2015). A precise time-step integration method by step-response and impulsive-response matrices for dynamic problems. International Journal for Numerical Methods in Engineering, 40(24), 4501–4527. Guo, P., He, W., Liang, L., Zhang, M., Lu, X., & Gong, X. (2019). Medical image fusion method based on guided filter. Chinese Journal of Liquid Crystals and Displays, 34(06), 605–612 (in Chinese). Guzzo, M., Agrebi, R., Espinosa, L., Baronian, G., Molle, V., Mauriello, E. M. F., et al. (2015). Evolution and design governing signal precision and amplification in a bacterial chemosensory pathway. PLOS Genetics, 11(8), e1005460. He, X., Sun, L., Lyu, C., & Wang, X. (2020). Quantum locally linear embedding for nonlinear dimensionality reduction. Quantum Information Processing, 19(9), 1–21. Hsiao, L.-F., Huang, X.-Y., Kuo, Y.-H., Chen, D.-S., Wang, H., Tsai, C.-C., et al. (2015). Blending of Global and Regional Analyses with a Spatial Filter: Application to Typhoon Prediction over the Western North Pacific Ocean. Weather and Forecasting, 30(3), 754–770. Hu, X., Pedrycz, W., & Wang, X. (2017). From fuzzy rule-based models to their granular generalizations. Knowledge-Based Systems, 124, 133–143. Jin, Z., Sun, Q., & Zhang, X. (2016). Cryocooler control algorithm design method based on system identification. Spacecraft Recovery & Remote Sensing, 37(1), 48–54 (in Chinese). Khanesar, A. M. (2016). A novel direct model reference fuzzy control approach based on observer and its applications. IFAC PapersOnLine, 49(13), 318–323. Koech, W. (2016). Parameter estimation of a DC motor-gear-alternator (MGA) system via step response methodology. American Journal of Applied Mathematics, 4(5), 252–257. Lian, J., Li, L., Liu, X., Huang, H., Zhou, Y., & Han, H. (2016). Research on adaptive control strategy optimization of hybrid electric vehicle. Journal of Intelligent & Fuzzy Systems, 30(5), 2581–2592. Li, L. J., Lu, W., & Qi, W. (2012). Improvement of Ladar imaging quality based on fusion technology. Lasers in Engineering, 23(1), 19–28. Liqiang, Z., Jianlin, W., Tao, Y., Kunyun, C., & Huan, J. Colored noise estimation algorithm based on autocovariance least-squares method. In 2015 12th IEEE international conference on electronic measurement & instruments (ICEMI) (pp. 481-486). IEEE. Liu, J. (2020). Model parameter identification and algorithm research of liquid level control system. Hangzhou Dianzi University, (in Chinese) Li, Q., & Wooldridge, J. (2011). Estimating Semiparametric Econometrics Models by Local Linear Method: With an Application to Cross-Country growth. Annals of Economics & Finance, 1(2), 337–357. Li, Z., Zhang, D., Dai, Y., Dong, J., Wu, L., Li, Y., et al. (2018). Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study. Chinese Journal of Cancer Research, 30(04), 16–24. Ll, N., Wang, J., Jl, Y., Liu, Y., & Zhu, Y. (2015). Predictive-control-based direct power control with an adaptive parameter identification technique for STATCOM. Power System Technology, 39(8), 2358–2363 (in Chinese). Machha Krishna, J. R., & Laxminidhi, T. (2019). Widely tunable low-pass \(g_{m}-C\) filter for biomedical applications. IET Circuits, Devices & Systems, 13(2), 239–244. Melo, A. G., Pinto, M. F., Marcato, A. L. M., Biundini, I. Z., & Rocha, N. M. S. (2021). Low-cost trajectory-based ball detection for impact indication and recording. Journal of Control, Automation and Electrical Systems, 32(2), 367–377. MK J. R., Polineni, S., Tonse, L. (2018). 91dB dynamic range 9.5 nW low pass filter for bio-medical applications. In 2018 IEEE computer society annual symposium on VLSI (ISVLSI) (pp. 453-457). IEEE. Moreira, A. C., Paredes, H. K. M., de Souza, W. A., et al. (2018). Evaluation of pattern recognition algorithms for applications on power factor compensation. Journal of Control, Automation and Electrical Systems, 29(1), 75–90. Narasimha, C., & Rao, A. N. (2020). Integrating Taylor-Krill herd-based SVM to fuzzy-based adaptive filter for medical image denoising. IET Image Processing, 14(3), 442–450. Nguyen, C. C., Ranasinghe, D. C., & Al-Sarawi, S. F. (2017). Analytical modeling and optimization of electret-based microgenerators under sinusoidal excitations. Microsysytem Technolohier, 23(12), 5855–5865. Ni, J., Xu, L., Ding, F., Gu, Y., Alsaedi, A., & Hayat, T. (2020). Parameter estimation for time-delay systems based on the frequency responses and harmonic balance methods. International Journal of Adaptive Control and Signal Processing, 34(12), 1779–1798. Oppenheim, A. V., Willsky, A. S., Nawab, S. H., & Hernández, G. M. (1998). Signals & Systems (Second Edition)[M]. New York, USA: Simon & Schuster Company. Pang, L. M., Tay, K. M., & Lim, C. P. (2016). Monotone fuzzy rule relabeling for the zero-order TSK fuzzy inference system. IEEE Transactions on Fuzzy Systems, 1-1. Park, G., Shim, Y., Jang, I., & Pack, S. (2016). Bloom-filter-aided redundancy elimination in opportunistic communications. IEEE Wireless Communications, 23(1), 112–119. Peng, N., Zhang, S., Guo, X., & Zhang, X. (2020). Online parameters identification and state of charge estimation for lithium-ion batteries using improved adaptive dual unscented Kalman filter. International Journal of Energy Research, 45(1), 975–990. Qin, X., Liu, J., Yu, C., Wang, Y., Zhang, H., & Sun, Y. (2020). Automatic identification of modal parameters based on interval perturbation and double-layer fuzzy clustering. Journal of Vibration and Shock, 39(23), 122-127+140 (in Chinese). Rao, G. H., Rekha, S. (2019) A 0.8-V, 55.1-dB DR, 100 Hz low-pass filter with low-power PTAT for bio-medical applications. IETE Journal of Research, (12), 1-11. Sakotic, Z., Crnojevic-Bengin, V., & Jankovic, N. (2017). Compact circular-patch-based bandpass filter for ultra-wideband wireless communication systems. AEU-International Journal of Electronics and Communications, 82, 272–278. Sá, E., Tchepel, O., Carvalho, A., & Borrego, C. (2015). Meteorological driven changes on air quality over Portugal: a KZ filter application. Atmospheric Pollution Research, 6(6), 979–989. Schmidt, S. F. (1981). The Kalman filter-Its recognition and development for aerospace applications. Journal of Guidance and Control, 4(1), 4–7. Shojaei-Asanjan, D., & Mansour, R. R. (2016). The Sky’s the limit: a switchable RF-MEMS filter design for wireless avionics intracommunication. IEEE Microwave Magazine, 18(1), 100–106. Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524. Soentoro, E. A. & Pebriana, N. (2019). Fuzzy rule-based model to optimize outflow in single reservoir operation. In 2019 MATEC web of conferences (pp. 04015). EDP Sciences. Sun, J., Lu, Q.-F., Zhao, Y., Zhang, P.-P., Wang, J., Wang, Q.-S., et al. (2019). A low-pass filter of 300 Hz improved the detection of pacemaker spike on remote and bedside electrocardiogram. Chinese Medical Journal, 132(5), 534–541. Suzuki, B. R., Yunpeng, Z., & Zi-Qiang, L. (2018). The analysis of nonlinear systems in the frequency domain using nonlinear output frequency response functions. Automatica, 94, 452–457. Szewczyk, R. (2019). Unified first order inertial element based model of magnetostrictive hysteresis and lift-off phenomenon. Materials, 12(10), 1689. Taheri, B., Sedaghat, M., Bagherpour, M. A., & Farhadi, P. (2019). A new controller for DC-DC converters based on sliding mode control techniques. Journal of Control, Automation and Electrical Systems, 30(1), 63–74. Tampére, C. M. & Immers, L. H. (2007) An extended Kalman filter application for traffic state estimation using CTM with implicit mode switching and dynamic parameters. In 2007 IEEE intelligent transportation systems conference (ITSC) (pp. 209-216). IEEE. Usman, M. (2019). Design of compact ultra-wideband monopole semi-circular patch Antenna for 5G wireless communication networks. Przeglad Elektrotechniczny, 1(4), 225–228. Wang, J., Shao, Y., Ge, Y., & Yu, R. (2020). Physical-layer authentication based on adaptive Kalman filter for V2X communication. Vehicular Communications, 26, 100281. Wang, W. L., Tian, J. F., & Cheung, W. S. (2020). Two-point boundary value problems for first order causal difference equations. Indian Journal of Pure and Applied Mathematics, 51(4), 1399–1416. Wu, Q., Wang, X., Hua, L., & Xia, M. (2021). Improved time optimal anti-swing control system based on low-pass filter for double pendulum crane system with distributed mass beam. Mechanical Systems and Signal Processing, 151, 107444. Yan, X., & Yan, L. (2006). Application of fuzzy pattern recognition to synthetical evaluation on electrical equipment capability. Journal of Jiangsu University of Science and Technology, 4, 38–41 (in Chinese). Yunxia, S., Yanli, Z., Ji, Z., Hang, J., & Yuanzhong, W. (2016). Study on the discrimination of FTIR spectroscopy of Gentiana Rigescenwith different harvest time. Spectroscopy and Spectral Analysis, 36(05), 1358–1362 (in Chinese). Zhang, F., Sanchez, B., Rutkove, S. B., Yang, Y., Zhong, H., Li, J., & Teng, Z. (2019). Numerical estimation of Fricke–Morse impedance model parameters using single-frequency sinusoidal excitation. Physiological Measurement, 40(9), 09NT01. Zhao, J. B., Netto, M., & Mili, L. (2017). A robust iterated extended Kalman filter for power system dynamic state estimation. IEEE Transactions on Power Systems, 32(4), 3205–3216. Zyprych-Walczak, J., Szabelska, A., Handschuh, L., Górczak, K., Klamecka, K., Figlerowicz, M., & Siatkowski, I. (2015). The impact of normalization methods on RNA-Seq data analysis. BioMed Research International, 2015, 1–10.