Optimization of a Fuzzy Automatic Voltage Controller Using Real-Time Recurrent Learning

Abstract

The automatic voltage regulator is an important component in energy generation systems; therefore, the tuning of this system is a fundamental aspect for the suitable energy conversion. This article shows the optimization of a fuzzy automatic voltage controller for a generation system using real-time recurrent learning, which is a technique conventionally used for the training of recurrent neural networks. The controller used consists of a compact fuzzy system based on Boolean relations, designed having equivalences with PI, PD, PID, and second order controllers. For algorithm implementation, the training equations are deduced considering the structure of the second order compact fuzzy controller. The results show that a closed-loop fuzzy control strategy was successfully implemented using real-time recurrent learning. In order to implement the controllers optimization, different weighting values for error and control action are used. The results show the behavior of the configurations used and its performance considering the steady state error, overshoot, and settling time.