Self-learning general purpose PID controller

Chunshien Li, Roland Priemer

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish
Pages (from-to)167-189
Number of pages23
JournalJournal of the Franklin Institute
Issue number2
StatePublished - Mar 1997


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