IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i4p683-d1595013.html
   My bibliography  Save this article

Multilayer Neurolearning of Measurement-Information-Poor Hydraulic Robotic Manipulators with Disturbance Compensation

Author

Listed:
  • Guichao Yang

    (School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China)

  • Zhiying Shi

    (School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China)

Abstract

In order to further improve the tracking performance of multiple-degree-of-freedom serial electro-hydraulic robotic manipulators, a high-performance multilayer neurocontroller will be proposed. In detail, multilayer neural networks will be employed to approximate the smooth and non-smooth state-dependent modeling uncertainties. Meanwhile, extended state observers will be utilized to estimate matched and unmatched time-varying disturbances. Moreover, these estimated values will be incorporated into the synthesized controller to compensate for the modeling uncertainties. Significantly, the proposed controller without “explosion of complexity” is suitable for the scene where the joint angular velocities are not measurable. Additionally, the sensor measurement noises can be reduced and input saturation nonlinearity will be handled.

Suggested Citation

  • Guichao Yang & Zhiying Shi, 2025. "Multilayer Neurolearning of Measurement-Information-Poor Hydraulic Robotic Manipulators with Disturbance Compensation," Mathematics, MDPI, vol. 13(4), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:4:p:683-:d:1595013
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/4/683/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/4/683/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:13:y:2025:i:4:p:683-:d:1595013. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.