An adaptive learning rate approach for an online neuro-fuzzy speed controller applied to a switched reluctance machine

The mostly used neuro-fuzzy motor speed control systems are time consuming and have an high computation effort when the speed reference changes gradually and the system has to learn the new operating point most of the time. In this cases a degradation of the system performance is evident has is demonstrated by experimental results in this paper. To surpass these effects, a decision and adaptation algorithm of the learning rate applied to the neuro-fuzzy control's approach is proposed. The adaptive learning rate algorithm with the controller is tested and compared in the speed control system for an 8/6 switched reluctance motor by experimental tests. The proposed solution is explained, tested and the experimental tests are presented and discussed.