Many variables in practical processes are unmeasurable or inaccurate due to the presence of disturbances, which significantly affect the control performance. Generally, an observer is often used to estimate such variables intervened by the noises. Extended state observer (ESO) has been widely used in designing control systems for its ability in rejecting uncertainties. In Zhao and Guo (2017), an ESO-based feedback control is proposed for MIMO systems to compensate the uncertainties. An event-triggered active disturbance reject control (ADRC) is proposed in Sun et al. (2017) to reduce the communication frequency with a satisfactory control performance. In Xue et al. (2015), an adaptive ESO-based ADRC is proposed for the control task of gasoline engines. A sliding mode control combined with ESO (Li et al. 2015) is proposed for the control of an electronic throttle valve. In order to deal with the parameter falling problem in motor system, an ESO-based dynamic surface control is proposed in Das and Talole (2016). Similarly, several composite controllers are designed in Kobayashi et al. (2007), Wei and Guo (2010), Guo and Wen (2011) using disturbance observer to estimate multiple disturbances. In Wei et al. (2016), the stochastic disturbance observer design is proposed for a class of stochastic systems.


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    Titel :

    ESO-based Data-Driven Iterative Learning Control


    Weitere Titelangaben:

    Intelligent Control & Learning Systems


    Beteiligte:
    Chi, Ronghu (Autor:in) / Hui, Yu (Autor:in) / Hou, Zhongsheng (Autor:in)


    Erscheinungsdatum :

    2022-11-16


    Format / Umfang :

    15 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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