Neuro-fuzzy implementation of a self-tuning fuzzy controller

Abstract

A self-tuning fuzzy PI controller (STFPIC) is designed elsewhere, using parallelly operated two rule-bases; one control rule-base, and the other gain rule-base, each having 49 rules. The output scaling factor (SF) of STFPIC is modified online by a gain updating factor following an operator's strategy. STFPlC is found to provide significantly improved performance for a wide range of processes. This study is an attempt for neuro-fuzzy implementations of STFPIC with considerably lesser number of rules, which are complete and capable of realizing almost similar performance as that of STFPIC. We consider two different structures of the proposed neuro-fuzzy PI controller (NFPIC); called NFPIC-1 and NFPIC-2, having only 50 and 49 rules respectively against 98 original rules of STFPIC. NFPIC-1 is similar in structure to that of STFPIC with two parallel rule-bases, each having 25 rules, whereas, the structure of NFPIC-2 is same as that of a conventional fuzzy controller with a single rule-base. Effectiveness of the developed neuro-fuzzy controllers (NFPIC-1 and NFPIC-2) is demonstrated using second-order linear as well as nonlinear processes.