Learning type PID control system using input dependence reinforcement scheme

Hideharu Sawada, Ji Sun Shin, Fumihiro Shoji, HeeHyol Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

PID control has widely used in the field of process control and a lot of methods have been used to design PID parameters. When the characteristic values of a controlled object are changed due to a change over the years or disturbance, the skilled operators observe the feature of the controlled responses and adjust the PID parameters using their knowledge and know-how, and a lot of labors are required to do it. In this research, we design a learning type PID control system using the stochastic automaton with learning function, namely learning automaton, which can autonomously adjust the control parameters updating the state transition probability using relative amount of controlled error. We show the effectiveness of the proposed learning type PID control system by simulations.

Original languageEnglish
Title of host publicationProceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08
Pages389-392
Number of pages4
Publication statusPublished - 2008
Event13th International Symposium on Artificial Life and Robotics, AROB 13th'08 - Oita
Duration: 2008 Jan 312008 Feb 2

Other

Other13th International Symposium on Artificial Life and Robotics, AROB 13th'08
CityOita
Period08/1/3108/2/2

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Keywords

  • Learning automaton
  • Learning control
  • PID control
  • State transition probability

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

Cite this

Sawada, H., Shin, J. S., Shoji, F., & Lee, H. (2008). Learning type PID control system using input dependence reinforcement scheme. In Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08 (pp. 389-392)