A Novel Recurrent Neural Network for Robot Control

Xin Luo*, Zhibin Li, Long Jin, Shuai Li

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

1 Citation (Scopus)

Abstract

To date, neural networks with high learning ability have been widely used in natural language processing, process control and other fields. In this chapter, a new recurrent neural network (RNN) is proposed to deal with time-varying underdetermined linear systems with disturbances, thereby achieving better control results. The related background of the underdetermined linear system is described in Sect. 3.1. In Sect. 3.2, we introduce the problem description. The theoretical analysis is discussed in Sect. 3.3. The experimental results are presented in Sect. 3.4. Finally, the conclusions and future research work are given in Sect. 3.5.
Original languageEnglish
Title of host publicationRobot Control and Calibration
Subtitle of host publicationInnovative Control Schemes and Calibration Algorithms
PublisherSpringer
Pages33-49
ISBN (Electronic)978-981-99-5766-8
ISBN (Print)978-981-99-5765-1
DOIs
Publication statusPublished - 2023
MoE publication typeA3 Part of a book or another research book

Publication series

SeriesSpringerBriefs in Computer Science
VolumePart F1465
ISSN2191-5768

Keywords

  • Double bound limits
  • Recurrent neural network
  • Residual errors
  • State variables
  • Time-varying underdetermined linear systems

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