Learning type PID control system using input dependence reinforcement scheme

Hideharu Sawada, Ji Sun Shin, Fumihiro Shoji, Hee Hyol Lee

Research output: Contribution to journalArticle

1 Citation (Scopus)

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
Pages (from-to)139-143
Number of pages5
JournalArtificial Life and Robotics
Volume13
Issue number1
DOIs
Publication statusPublished - 2008 Dec 1

Keywords

  • Learning Automaton
  • Learning control
  • PID control
  • State transition probability

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence

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