A New Neurofuzzy Controller Based on NFN Networks

Abstract

This work presents a new online learning controller, the ONFC (Online Neurofuzzy Controller), which has as base the Neo Fuzzy Neuron-NFN. Its principal difference from the most of the neurofuzzy structure used in control systems is the fact that the process error is not only used to correct the network parameters, but also as network input. Moreover, the ONFC has a very simple structure with only one input and one output, associated by two fuzzy rules. Other important characteristic presented by this controller is the reduced effort for the fixed parameters adjustment. The proposed controller development is presented for single and multi-loop control. This controller is applied for the control of two different plants. In a single loop control, simulations results are obtained for a generic plant with reverse characteristic. In a multi-loop control, the controller performance is evaluated through a practical implementation of an induction motor vector control with stator field orientation.