Self-learning general purpose PID controller

Chunshien Li, Roland Priemer

研究成果: 雜誌貢獻期刊論文同行評審

4 引文 斯高帕斯(Scopus)

摘要

A self-learning Neural-net-based Fuzzy logic System (NFS) is designed to determine the gains of a PID controller. The controller operates in a closed-loop system. The NFS receives the error, error integral and error derivative signals, and by fuzzy inference it adjusts the controller gains. As a result, these gains vary with time to achieve good performance compared to a conventional PID controller. A modified random optimization learning algorithm is given to train the NFS. The learning algorithm does not require a model of the plant being controlled. Instead, it uses knowledge of plant input/output behavior to update parameters of the NFS.

原文???core.languages.en_GB???
頁(從 - 到)167-189
頁數23
期刊Journal of the Franklin Institute
334
發行號2
DOIs
出版狀態已出版 - 3月 1997

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