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

Abstract

One characteristic of neuro-fuzzy systems is the possibility of incorporating preliminary information in their structure as well as being able to establish an initial configuration to carry out the training. In this regard, the strategy to establish the configuration of the fuzzy system is a relevant aspect. This document displays the design and implementation of a neuro-fuzzy controller based on Boolean relations to regulate the angular position in an electromechanical plant, composed by a motor coupled to inertia with friction (a widely studied plant that serves to show the control system design process). The structure of fuzzy systems based on Boolean relations considers the operation of sensors and actuators present in the control system. In this way, the initial configuration of fuzzy controller can be determined. In order to perform the optimization of the neuro-fuzzy controller, the continuous plant model is converted to discrete time to be included in the closed-loop controller training equations. For the design process, first the optimization of a Proportional Integral (PI) linear controller is carried out. Thus, linear controller parameters are employed to establish the structure and initial configuration of the neuro-fuzzy controller. The optimization process also includes weighting factors for error and control action in such a way that allows having different system responses. Considering the structure of the control system, the optimization algorithm (training algorithm) employed is dynamic back propagation. The results via simulations show that optimization is achieved in the linear and neuro-fuzzy controllers using different weighting values for the error signal and control action. It is also observed that the proposed control strategy allows disturbance rejection.

An Autonomous Vehicle Stability Control Using Active Fault-Tolerant Control Based on a Fuzzy Neural Network

“…The simplicity with which stability analysis in closed-loop may be performed using the Lyapunov theory has inspired greater attention. Thus, in general, this technique is based on one of the fuzzy observer paradigms for the FDI task, and an augmented state space representation is derived by adopting the error dynamics between the output and the estimated state, in such a way that the fixed gains ensure the fuzzy controller and observer's asymptotic stability [28]. The TS representation of the equation above is given by:…”

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

“…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. It is noteworthy that works in references [29,30] consider linear models of plants.…”

“…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.…”