Data-Driven Control Based on Information Concentration Estimator and Regularized Online Sequential Extreme Learning Machine

Xiaofei Zhang, Hongbin Ma*, Huaqing Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Due to the complexity of digital equipment and systems, it is quite difficult to obtain a precise mechanism model in practice. For an unknown discrete-time nonlinear system, in this paper, a semi-parametric model is used to describe this discrete-time nonlinear system, and this semi-parametric model contains a parametric uncertainty part and a nonparametric uncertainty part. Based on this semi-parametric model, a novel data-driven control algorithm based on an information concentration estimator and regularized online sequential extreme learning machine (ReOS-ELM) is designed. The information concentration estimator estimates the parametric uncertainty part; The training data of ReOS-ELM network is obtained, based on symmetry and information concentration estimator, then the training of ReOS-ELM network and the estimate of nonparametric uncertainty part using ReOS-ELM network are carried out online, successively. A stability analysis and three simulation examples were performed, and the simulation results show that the proposed data-driven control algorithm is effective in improving the control accuracy.

Original languageEnglish
Article number88
JournalSymmetry
Volume16
Issue number1
DOIs
Publication statusPublished - Jan 2024

Keywords

  • data-driven control
  • extreme learning machine
  • information concentration estimator
  • unknown discrete-time systems

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