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.

Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant

“…In this way, fuzzy control design consists of modifying (extending) a linear (discrete time) controller using fuzzy sets to model the controller non-linear relations. This design principle is used in [23] to control an automatic voltage regulator. The general diagram of the control system can be seen in Figure 9.…”

“…In this way, fuzzy systems with compact structures can be established and used for identification and control of dynamic systems [21]. As an example, the application of this type of system is shown in [23] to control an Automatic Voltage Regulator (AVR). In control applications, the Dynamic Back Propagation (DBP) training algorithm is used in cases when the plant's feedback signals and the controller are required for the neuro-fuzzy control system.…”

Filling Control of a Conical Tank Using a Compact Neuro-Fuzzy Adaptive Control System

“…In previous implementations using fuzzy systems-based boolean relations, in [27] the adaptive control for a power distribution system is implemented, and in [28] the control of a MIMO system is performed. In the training of the neuro-fuzzy controller, in [29] a system to control an automatic voltage regulator is designed and optimized by employing architecture II for the controller. Meanwhile, reference [30] displays the optimization of a PI controller based on architecture II.…”

“…Meanwhile, reference [30] displays the optimization of a PI controller based on architecture II. It is noteworthy that works in references [29,30] consider linear models of plants.…”