Feedback-Error-Learning in pelletizing plant control - Ribeiro, Paulo Rogério de Almeida; Costa, Tarcisio Souza; Barros, Victor Hugo; Almeida Neto, Areolino de; Oliveira, Alexandre César Muniz de (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2009: Anais do VII Encontro Nacional de Inteligência Artificial, 2009)
This work is devoted to present a process control application in an industrial process of iron pellet cooking in an important mining company in Brazil. This work uses an adaptive control in order to improve the performance of the conventional controller already installed in the plant. The main strategy approached here is known as Feedback-Error-Learning (FEL), in which a neural network (NN) learns to improve the control actuation of a Proportional-Integral-Derivative (PID) controller. The advantage of the FEL strategy is to provide cooperation between the adaptive controller and the conventional controller, in order that the NN learns not only the actuation necessary for the control, but new actions can be acquired as consequence of changes in the process. A second control strategy is also employed as alternative for the conventional PID control: a Proportional Integrative Logic Fuzzy Controller (PI-FLC). Fuzzy controllers have been satisfactorily used in presence of non-linearity or absence of a precise mathematical model to address the changes in system state. In this work, due to the unknown mathematic model of the plant and, in order to simulate the control of the process, a neural model of the plant is also presented. In a simulation environment, conventional PID, FEL and PI-FLC strategies are compared and the results are discussed.