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Nonlinear-disturbance-observer-based predictive control for trajectory tracking of planar motors

  • Su Dan Huang
  • , Zhi Hui Xu
  • , Guang Zhong Cao
  • , Chao Wu
  • , Jiangbiao He

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

To improve the trajectory tracking performance of planar motors against disturbances, model predictive position control (MPPC) methods using the non-linear disturbance observer (NDO) are proposed in this study. Based on the single-axis dynamic model with disturbances, a single-axis NDO is designed using an extended state observer approach. The designed NDO is expressed as a third-order non-linear state-space equation in which the position error, velocity error, and lumped disturbance in the single axis are taken as the state variables. Two MPPC methods are developed based on the NDO. In the first MPPC, the disturbance is embedded into the prediction model using the NDO, and a controller is designed to minimise a quadratic cost function, which is established by applying the prediction model with disturbance. The output of the controller is the control action. In the second MPPC, a controller is used to minimise the quadratic cost function, which is built by employing the prediction model without disturbance. The sum of the output of the controller and the compensated disturbance estimated by the NDO is the control action. The comparative experiment is performed on a planar motor system self-developed in the laboratory. The effectiveness of the proposed methods is verified via the experimental results.

Original languageEnglish
Pages (from-to)389-399
Number of pages11
JournalIET Electric Power Applications
Volume18
Issue number4
DOIs
StatePublished - Apr 2024

Bibliographical note

Publisher Copyright:
© 2023 The Authors. IET Electric Power Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 52277061 and 51907128, in part by the Natural Science Foundation of Guangdong Province, China under Grant 2021A1515011704, and in part by the Shenzhen Science and Technology Program, China under Grant JCYJ20220818095804009 and 20220809200041001.

FundersFunder number
National Natural Science Foundation of China (NSFC)51907128, 52277061
Science, Technology and Innovation Commission of Shenzhen MunicipalityJCYJ20220818095804009, 20220809200041001
Natural Science Foundation of Guangdong Province2021A1515011704

    Keywords

    • linear motors
    • motion control
    • position control
    • predictive control

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering

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