A Novel LCOT Control Strategy for Self-Driving Electric Mobile Robots

Hwa Dong Liu, Guo Jyun Gao, Shiue Der Lu*, Yi Hsuan Hung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This study proposes a novel logarithm curve and operating time (LCOT) control strategy for a self-driving electric mobile robot. This new LCOT control strategy enables the mobile robot to speed up and slow down mildly when running longitudinally, turning left, turning right, and encountering an obstacle based on the relationship between the logarithm curve and operating time. This novel control strategy can enhance the comfort and stability of the self-driving electric mobile robot and reduce its vibrations and instabilities in the operation process. The proposed LCOT control strategy and the fixed duty cycle method were verified experimentally. The results showed that the LCOT control strategy spent 300 s running on a 3000 cm road, whereas the fixed duty cycle method spent 450 s. Because this novel method controls the acceleration and deceleration of the self-driving electric mobile robot gently and flexibly, the proposed LCOT control strategy has better working efficiency than the fixed duty cycle method. This novel control strategy is simple and easy to be implemented. As it can reduce the working load of the controller, increase system efficiency, and require low cost, it can be effectively used in a self-driving electric mobile robot.

Original languageEnglish
Article number9178
JournalEnergies
Volume15
Issue number23
DOIs
Publication statusPublished - 2022 Dec

Keywords

  • acceleration and deceleration
  • fixed duty cycle method
  • logarithm curve and operating time control strategy
  • self-driving electric mobile robot

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Fuel Technology
  • Engineering (miscellaneous)
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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