Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3366194.3366205acmotherconferencesArticle/Chapter ViewAbstractPublication PagesricaiConference Proceedingsconference-collections
research-article

Dynamic surface asymptotic tracking of uncertain nonlinear systems with backlash-like hysteresis

Published: 20 September 2019 Publication History
  • Get Citation Alerts
  • Abstract

    In this paper, a dynamic surface asymptotic tracking control problem is investigated for a class of uncertain nonlinear systems preceded by unknown backlash-like hysteresis. By introducing the new modified nonlinear filters, a novel adaptive control algorithm via dynamic surface approach is therefore proposed. Moreover, the compensating term is considered to compensate the boundary layer errors in the filters, which can improve the control performance. Then, the positive time-varying integral function of hysteresis and external disturbance is utilized to design a smooth adaptive controller. It is proved that the constructed controller can guarantee semi-global stability of the closed-loop system and makes the convergence of the tracking error to origin theoretically. Finally, simulation results for a second-order controlled system are conducted to illustrate the effectiveness of the proposed the scheme.

    References

    [1]
    EC Stoner and EP Wohlfarth (1948). A mechanism of magnetic hysteresis in heterogeneous alloys. IEEE Transactions on Magnetics, 27(4), 3475--3518.
    [2]
    MA Janaideh, M Rakotondrabe, and X Tan (2016). Guest Editorial Focused Section on Hysteresis in Smart Mechatronic Systems: Modeling, Identification, and Control. IEEE/ASME Transactions on Mechatronics, 21(1), 1--3.
    [3]
    G Tao and PV Kokotovic (1995). Adaptive control of plants with unknown hysteresis. IEEE Transactions on Automatic Control, 40(1), 200--212.
    [4]
    CY Su, Y Stepanenko, J Svoboda, and TP Leung (2000). Robust adaptive control of a class of nonlinear systems with unknown backlash-like hysteresis. IEEE Transactions on Automatic Control, 45(12), 2427--2432.
    [5]
    GY Gu, MJ Yang, and LM Zhu (2012). Real-time inverse hysteresis compensation of piezoelectric actuators with a modified Prandtl-Ishlinskii model. The Review of Scientific Instruments, 83(6), 065106.
    [6]
    Z Li, J Shan, and U Gabbert (2018). Inverse Compensation of Hysteresis Using Krasnoselskii-Pokrovskii Model. IEEE/ASME Transactions on Mechatronics, 23(2), 966--971.
    [7]
    Y. Liu, X. Hu, and L. Huang, Adaptive asymptotic tracking of uncertain nonlinear systems with unknown hysteresis nonlinearity, https://ieeexplore.ieee.org/abstract/document/7967376.
    [8]
    J Zhou, C Wen, and Y Zhang (2004). Adaptive backstepping control of a class of uncertain nonlinear systems with unknown backlash-like hysteresis. IEEE Transactions on Automatic Control, 49(10), 1751--1759.
    [9]
    J Zhou, C Zhang, and C Wen (2007). Robust Adaptive Output Control of Uncertain Nonlinear Plants with Unknown Backlash Nonlinearity. IEEE Transactions on Automatic Control, 52(3), 503--509.
    [10]
    Y Li, S Tong, and T Li (2012). Adaptive fuzzy output feedback control of uncertain nonlinear systems with unknown backlash-like hysteresis. Information Sciences, 198(none), 130--146.
    [11]
    W Lv and F Wang (2018). Finite-Time Adaptive Fuzzy Tracking Control for a Class of Nonlinear Systems with Unknown Hysteresis. International Journal of Fuzzy Systems, 20(3), 782--790.
    [12]
    HQ Wang, HK Shen, XJ Xie, T Hayat, and FE Alsaadi (2018). Robust adaptive neural control for pure-feedback stochastic nonlinear systems with Prandtl-Ishlinskii hysteresis. Neurocomputing, 314, 169--176.
    [13]
    XJ Wang, XH Yin, QH Wu, and FQ Meng (2018). Adaptive neural tracking control for nonstrict-feedback nonlinear systems with unknown backlash-like hysteresis and unknown control directions. International Journal of Robust and Nonlinear Control, 28(16), 5140--5157.
    [14]
    D Swaroop, JK Hedrick, PP Yip, and JC Gerdes (2000). Dynamic surface control for a class of nonlinear systems. IEEE Transactions on Automatic Control, 45(10), 1893--1899.
    [15]
    SJ Yoo, JB Park, and YH Choi (2007). Adaptive Dynamic Surface Control for Stabilization of Parametric Strict-Feedback Nonlinear Systems with Unknown Time Delays. IEEE Transactions on Automatic Control, 52(12), 2360--2365.
    [16]
    G Sun, D Wang, XQ Li, and ZH Peng (2013). A DSC approach to adaptive neural network tracking control for pure-feedback nonlinear systems. Applied Mathematics and Computation, 219(11), 6224--6235.
    [17]
    L Liu, ZS Wang, and HG Zhang (2016). Adaptive dynamic surface error constrained control for MIMO systems with backlash-like hysteresis via prediction error technique. Nonlinear Dynamics, 84(4), 1989--2002.
    [18]
    X Zhang, C Su, Y Lin, L Ma, and J Wang (2015). Adaptive Neural Network Dynamic Surface Control for a Class of Time-Delay Nonlinear Systems with Hysteresis Inputs and Dynamic Uncertainties. IEEE Transactions on Neural Networks and Learning Systems, 26(11), 2844--2860.
    [19]
    YP Pan and HY Yu (2015). Dynamic surface control via singular perturbation analysis. Automatica, 57 (Complete), 29--33.
    [20]
    CC Su, Y Stepanenko, J Svoboda, and TP Leung (2000). Robust adaptive control of a class of nonlinear systems with unknown backlash-like hysteresis. IEEE Transactions on Automatic Control, 45(12), 2427--2432.
    [21]
    XJ Wang, XH Yin, and F Shen (2018). Disturbance observer based adaptive neural prescribed performance control for a class of uncertain nonlinear systems with unknown backlash-like hysteresis. Neurocomputing, 299, 10--19.
    [22]
    XY Zhang, Z Li, CY Su, XK Chen, JG Wang, and LL Xia (2016). Robust Adaptive Neural Control for a Class of Time-Varying Delay Systems with Backlash-like Hysteresis Input. Asian Journal of Control, 18(3), 1087--1101.
    [23]
    B Xu, YY Guo, Y Yuan, YH Fan, and DW Wang (2016). Fault-tolerant control using command-filtered adaptive backstepping technique: Application to hypersonic longitudinal flight dynamics. International Journal of Adaptive Control and Signal Processing, 30(4), 553--577.
    [24]
    YH Liu (2016). Adaptive tracking control for a class of uncertain pure-feedback systems. International Journal of Robust and Nonlinear Control, 26(5), 1143--1154.
    [25]
    Z Zhang, S Xu, and B Zhang (2014). Asymptotic Tracking Control of Uncertain Nonlinear Systems with Unknown Actuator Nonlinearity. IEEE Transactions on Automatic Control, 59(5), 1336--1341.
    [26]
    M Krstic, I Kanellakopoulos, and PV Kokotovic (1995). Nonlinear and Adaptive Control. Lecture Notes in Control & Information Sciences, 5(2), 4475--4480.

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence
    September 2019
    803 pages
    ISBN:9781450372985
    DOI:10.1145/3366194
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 September 2019

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Adaptive control
    2. Asymptotic tracking
    3. Backlash-like hysteresis
    4. Dynamic surface control

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • the National Natural Science Foundation of China

    Conference

    RICAI 2019

    Acceptance Rates

    RICAI '19 Paper Acceptance Rate 140 of 294 submissions, 48%;
    Overall Acceptance Rate 140 of 294 submissions, 48%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 40
      Total Downloads
    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)0

    Other Metrics

    Citations

    View Options

    Get Access

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media