Abstract
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 language | English |
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Pages (from-to) | 167-189 |
Number of pages | 23 |
Journal | Journal of the Franklin Institute |
Volume | 334 |
Issue number | 2 |
DOIs | |
State | Published - Mar 1997 |