Research article Topical Sections

Frequency analysis of a discrete-time fast nonlinear tracking differentiator algorithm based on isochronic region method

  • Received: 14 July 2024 Revised: 19 August 2024 Accepted: 27 August 2024 Published: 02 September 2024
  • In fault detection, feedback control, and other fields, real-time differential estimation of a given signal in a complex noise environment is an important but challenging task. In this paper, a discrete-time fast nonlinear tracking differentiator (FNTD) based on hyperbolic tangent functions was proposed. To start, the differential signal acquisition problem was equated to the time-optimal control (TOC) law for constructing a double-integral system using a state feedback approach. Next, the FNTD algorithm based on the hyperbolic tangent function was presented by utilizing the isochronic region (IR) method in the discrete time domain. Then, the frequency-domain characteristics of the FNTD were analyzed and the rule for tuning the parameters was provided by the frequency scan test method. Finally, the simulation results demonstrated that the proposed FNTD had fast and accurate tracking performance, as well as excellent filtering and differential extraction capability compared with other differentiators.

    Citation: Zhizhou Zhang, Yueliang Pan, Weilong Zhao, Jinchu Zhang, Zheng Zi, Yuan Xie, Hehong Zhang. Frequency analysis of a discrete-time fast nonlinear tracking differentiator algorithm based on isochronic region method[J]. Electronic Research Archive, 2024, 32(9): 5157-5175. doi: 10.3934/era.2024238

    Related Papers:

  • In fault detection, feedback control, and other fields, real-time differential estimation of a given signal in a complex noise environment is an important but challenging task. In this paper, a discrete-time fast nonlinear tracking differentiator (FNTD) based on hyperbolic tangent functions was proposed. To start, the differential signal acquisition problem was equated to the time-optimal control (TOC) law for constructing a double-integral system using a state feedback approach. Next, the FNTD algorithm based on the hyperbolic tangent function was presented by utilizing the isochronic region (IR) method in the discrete time domain. Then, the frequency-domain characteristics of the FNTD were analyzed and the rule for tuning the parameters was provided by the frequency scan test method. Finally, the simulation results demonstrated that the proposed FNTD had fast and accurate tracking performance, as well as excellent filtering and differential extraction capability compared with other differentiators.



    加载中


    [1] J. Han, From pid to active disturbance rejection control, IEEE Trans. Ind. Electron., 56 (2009), 900–906. https://doi.org/10.1109/TIE.2008.2011621 doi: 10.1109/TIE.2008.2011621
    [2] H. Ríos, E. Punta, L. Fridman, Fault detection and isolation for nonlinear non-affine uncertain systems via sliding-mode techniques, Int. J. Control, 90 (2017), 218–230. https://doi.org/10.1080/00207179.2016.1173727 doi: 10.1080/00207179.2016.1173727
    [3] C. Vázquez, S. Aranovskiy, L. B. Freidovich, L. M. Fridman, Time-varying gain differentiator: A mobile hydraulic system case study, IEEE Trans. Control Syst. Technol., 24 (2016), 1740–1750. https://doi.org/10.1109/TCST.2015.2512880 doi: 10.1109/TCST.2015.2512880
    [4] W. Ji, D. Lv, S. Luo, Y. Sun, Multiple models-based fault tolerant control of levitation module of maglev vehicles against partial actuator failures, IEEE Trans. Veh. Technol., 2024 (2024). https://doi.org/10.1109/TVT.2024.3399235
    [5] H. Zhang, G. Xiao, Y. Xie, W. Guo, C. Zhai, Tracking Differentiator Algorithms, Springer, 2021. https://doi.org/10.1007/978-981-15-9384-0
    [6] R. P. Borase, D. Maghade, S. Sondkar, S. Pawar, A review of pid control, tuning methods and applications, Int. J. Dyn. Control, 9 (2021), 818–827. https://doi.org/10.1007/s40435-020-00665-4 doi: 10.1007/s40435-020-00665-4
    [7] H. Zhang, G. Xiao, Y. Xie, W. Guo, C. Zhai, H. Zhang, et al., Tracking differentiators in real-life engineering, in Tracking Differentiator Algorithms. Lecture Notes in Electrical Engineering, 717 (2021), 77–90. https://doi.org/10.1007/978-981-15-9384-0_7 doi: 10.1007/978-981-15-9384-0_7
    [8] H. Feng, S. Li, A tracking differentiator based on taylor expansion, Appl. Math. Lett., 26 (2013), 735–740. https://doi.org/10.1016/j.aml.2013.02.003 doi: 10.1016/j.aml.2013.02.003
    [9] H. Wu, J. Huang, Control of induction motor drive based on adrc and inertia estimation, in 2019 IEEE International Electric Machines and Drives Conference (IEMDC), IEEE, (2019), 1607–1612. https://doi.org/10.1109/IEMDC.2019.8785393
    [10] A. Levant, X. Yu, Sliding-mode-based differentiation and filtering, IEEE Trans. Autom. Control, 63 (2018), 3061–3067. https://doi.org/10.1109/TAC.2018.2797218 doi: 10.1109/TAC.2018.2797218
    [11] A. Levant, M. Livne, X. Yu, Sliding-mode-based differentiation and its application, IFAC-PapersOnLine, 50 (2017), 1699–1704. https://doi.org/10.1016/j.ifacol.2017.08.495 doi: 10.1016/j.ifacol.2017.08.495
    [12] W. Bai, W. Xue, Y. Huang, H. Fang, On extended state based kalman filter design for a class of nonlinear time-varying uncertain systems, Sci. China Inf. Sci., 61 (2018), 1–16. https://doi.org/10.1007/s11432-017-9242-8 doi: 10.1007/s11432-017-9242-8
    [13] J. Yu, S. Jin, Sliding mode tracking differentiator with adaptive gains for filtering and derivative estimation of noisy signals, IEEE Access, 9 (2021), 86017–86024. https://doi.org/10.1109/ACCESS.2021.3088544 doi: 10.1109/ACCESS.2021.3088544
    [14] Y. Liu, L. Hao, Adaptive tracking differentiator control for nonlinear stochastic systems, in 2022 13th Asian Control Conference (ASCC), (2022), 512–517. https://doi.org/10.23919/ASCC56756.2022.9828327
    [15] H. Zhang, Y. Xie, G. Xiao, C. Zhai, Z. Long, A simple discrete-time tracking differentiator and its application to speed and position detection system for a maglev train, IEEE Trans. Control Syst. Technol., 27 (2018), 1728–1734. https://doi.org/10.1109/TCST.2018.2832139 doi: 10.1109/TCST.2018.2832139
    [16] X. Wang, S. Jin, High-order sliding mode tracking differentiator with neural network based adaptive parameter estimation, in Journal of Physics: Conference Series, 2613 (2023), 012013. https://doi.org/10.1088/1742-6596/2613/1/012013
    [17] Y. Feng, Z. Li, Y. Liu, Z. He, H. Li, Differentiator-based adaptive h$\infty$ tracking control of fully actuated systems, in 2024 3rd Conference on Fully Actuated System Theory and Applications (FASTA), IEEE, (2024), 680–684. https://doi.org/10.1109/FASTA61401.2024.10595180
    [18] A. M. Dabroom, H. K. Khalil, Output feedback sampled-data control of nonlinear systems using high-gain observers, IEEE Trans. Autom. Control, 46 (2001), 1712–1725. https://doi.org/10.1109/9.964682 doi: 10.1109/9.964682
    [19] X. Wang, Z. Chen, Z. Yuan, Design and analysis for new discrete tracking-differentiators, Appl. Math. J. Chin. Univ., 18 (2003), 214–222. https://doi.org/10.1007/s11766-003-0027-0 doi: 10.1007/s11766-003-0027-0
    [20] A. Levant, Robust exact differentiation via sliding mode technique, Automatica, 34 (1998), 379–384. https://doi.org/10.1016/S0005-1098(97)00209-4 doi: 10.1016/S0005-1098(97)00209-4
    [21] A. Abdessameud, A. Tayebi, Global trajectory tracking control of vtol-uavs without linear velocity measurements, Automatica, 46 (2010), 1053–1059. https://doi.org/10.1016/j.automatica.2010.03.010 doi: 10.1016/j.automatica.2010.03.010
    [22] L. Zhang, Z. Zhang, L. Huang, Hybrid non-linear differentiator design for a permanent-electro magnetic suspension maglev system, IET Signal Process., 6 (2012), 559–567. https://doi.org/10.1049/iet-spr.2011.0264 doi: 10.1049/iet-spr.2011.0264
    [23] J. Han, L. Yuan, The discrete form of tracking-differentiator, J. Syst. Sci. Math. Sci., 19 (1999), 263–273. https://doi.org/10.12341/jssms09882 doi: 10.12341/jssms09882
    [24] Z. Gao, On discrete time optimal control: A closed-form solution, in Proceedings of the 2004 American Control Conference, IEEE, 1 (2004), 52–58. https://doi.org/10.23919/ACC.2004.1383578
    [25] Z. Lu, S. Bai, M. Jiang, Y. Xu, F. Liu, Improved design of linear self-turbulent permanent magnet synchronous motor speed controller, J. Electron. Meas. Instrum., 36 (2023), 73–81.
    [26] Z. Hao, Y. Yang, Y. Gong, Z. Hao, C. Zhang, H. Song, et al., Linear/nonlinear active disturbance rejection switching control for permanent magnet synchronous motors, IEEE Trans. Power Electron., 36 (2021), 9334–9347. https://doi.org/10.1109/TPEL.2021.3055143 doi: 10.1109/TPEL.2021.3055143
    [27] J. Li, H. Ren, Y. Zhong, Robust speed control of induction motor drives using first-order auto-disturbance rejection controllers, IEEE Trans. Ind. Appl., 51 (2014), 712–720. https://doi.org/10.1109/TIA.2014.2330062 doi: 10.1109/TIA.2014.2330062
    [28] H. Zhang, G. Xiao, X. Yu, Y. Xie, On convergence performance of discrete-time optimal control based tracking differentiator, IEEE Trans. Ind. Electron., 68 (2020), 3359–3369. https://doi.org/10.1109/TIE.2020.2979530 doi: 10.1109/TIE.2020.2979530
    [29] L. Zhao, H. Cheng, J. Zhang, Y. Xia, Angle attitude control for a 2-dof parallel mechanism of pmas using tracking differentiators, IEEE Trans. Ind. Electron., 66 (2018), 8659–8669. https://doi.org/10.1109/TIE.2018.2884215 doi: 10.1109/TIE.2018.2884215
    [30] Y. Xie, Y. Li, L. She, P. Cui, C. Dai, A discrete second-order nonlinear tracking-differentiator based on boundary characteristic curves, Inf. Control, 43 (2014), 257–263. https://doi.org/10.3724/SP.J.1219.2014.00257 doi: 10.3724/SP.J.1219.2014.00257
    [31] Y. Xie, Y. Li, Z. Long, C. Dai, Discrete second-order nonlinear tracking-differentiator based on boundary characteristic curves and variable characteristic points and its application to velocity and position detection system, Acta Autom. Sin., 40 (2014), 952–964. http://dx.doi.org/10.3724/SP.J.1004.2014.00952 doi: 10.3724/SP.J.1004.2014.00952
    [32] H. Zhang, Y. Xie, G. Xiao, C. Zhai, Z. Long, A simple discrete-time tracking differentiator and its application to speed and position detection system for a maglev train, IEEE Trans. Control Syst. Technol., 27 (2019), 1728–1734. https://doi.org/10.1109/TCST.2018.2832139 doi: 10.1109/TCST.2018.2832139
    [33] M. Athans, P. L. Falb, Optimal Control: An Introduction to the Theory and Its Applications, Courier Corporation, 2007.
    [34] D. E. Kirk, Optimal Control Theory: An Introduction, Courier Corporation, 2004.
    [35] J. Sun, K. Hang, Analysis and synthesis of time-optimal control systems, IFAC Proc. Volumes, 1 (1963), 347–351. https://doi.org/10.1016/S1474-6670(17)69673-3 doi: 10.1016/S1474-6670(17)69673-3
    [36] X. Wang, Rapid-convergent nonlinear differentiator, Mech. Syst. Signal Process., 28 (2012), 414–431. https://doi.org/10.1016/j.ymssp.2011.09.026 doi: 10.1016/j.ymssp.2011.09.026
    [37] W. Chen, J. Yang, L. Guo, S. Li, Disturbance-observer-based control and related methods–-an overview, IEEE Trans. Ind. Electron., 63 (2015), 1083–1095. https://doi.org/10.1109/TIE.2015.2478397 doi: 10.1109/TIE.2015.2478397
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(474) PDF downloads(30) Cited by(0)

Article outline

Figures and Tables

Figures(8)  /  Tables(5)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog