Self-learning control of load changes in motor-driven load simulator using CMAC

Jianfu Li, Wenxing Fu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

How to retain the high load precision of a motor-driven load simulator in the case of great change in load gradient is one of its key problems. In the past, the compound PID control method was used to improve its load precision. However, because of the influence of its time-varying character and non-linearity, the method does not produce ideal load speed or precision. Taking the characteristics of the load simulator into account, the paper applies the CMAC neural-network control structure to the load simulator and presents its control structure and algorithm. The analysis of the experimental results, given in Figs. 5 and 6 and Table 2, indicates preliminarily that our method overcomes the shortcomings of the sole use of PID control method and satisfies the requirements for high-precision in the case of great changes in load gradient.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Pages156-159
Number of pages4
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009 - Shanghai, China
Duration: 20 Nov 200922 Nov 2009

Publication series

NameProceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Volume1

Conference

Conference2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Country/TerritoryChina
CityShanghai
Period20/11/0922/11/09

Keywords

  • CMAC neural network
  • Load gradient
  • Load precision
  • Motor-driven load simulator
  • PID control method

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