ESTRATÉGIAS DE CONTROLE PARA USINA DE PELOTIZAÇÃO DA VALE

Este trabalho destina-se a apresentar estratégias de controle para melhorias no desempenho do sistema de controle da usina de pelotização da Vale em São Luís do Maranhão, Brasil. A usina já detém um sistema de controle convencional, Proporcional Integral Derivativo (PID). No intuito de melhorar o desempenho faz-se uso da estratégia Feedback-Error-Learning (FEL), assim como da estratégia Multi-Network-Feedback-Error-Learning (MNFEL). No intuito de comparar estratégias não convencionais faz-se uso também de um controlador PI-Fuzzy. Estas estratégias são comparadass e seus resultados discutidos.Palavras-chave: Controle adaptativo. Redes neurais. Lógica Fuzzy.AbstractThis work is intended to provide control strategies for improvements in the control system of the Vale pellet plant in São Luis, Maranhão, Brazil. The current installed plant control is a conventional Proportional Integral Derivative (PID) well suitable for regular day-by-day operations, but featuring bad performance for occasional plant re-start. In order to improve the control performance, we are proposing the use of Feedback-Error-Learning (FEL) and Multi-Network-Feedback-Error-Learning (MNFEL) strategies, since both can coupled together current PID control. In order to provide a wide comparison, other unconventional strategies, as PI-Fuzzy controller, also are included in the experiments and discussion of this work.Keywords: Adaptive control. Neural networks. Fuzzy logic

Universidade Federal do Maranhäo

Abstract: this article is a continuation of the previous article called “Obstacle Avoidance in Dynamic Environment: a Hierarchical Solution”, which presented the concept for obstacle avoidance in dynamic environment suitable for mobile robot. The task of obstacle avoidance is divided in three principal groups: local, global and for emergencies. The global avoidance is here approached, in which the concept used is based on reinforcement learning and on an algorithm for planning the avoidance, in such a way that the situations are divided into four states and two kinds of actions are possible. The states define in what situation the movement relationship between the robot and the dynamic obstacles is present, and the actions decide in which direction the robot must follow, in order to avoid a possible collision. The algorithm plans in order to decide an action, which certainly is not the best for each obstacle, but normally is the best considering the group

xRatSLAM: An Extensible RatSLAM Computational Framework

Simultaneous localization and mapping (SLAM) refers to techniques for autonomously constructing a map of an unknown environment while, at the same time, locating the robot in this map. RatSLAM, a prevalent method, is based on the navigation system found in rodent brains. It has served as a base algorithm for other bioinspired approaches, and its implementation has been extended to incorporate new features. This work proposes xRatSLAM: an extensible, parallel, open-source framework applicable for developing and testing new RatSLAM variations. Tests were carried out to evaluate and validate the proposed framework, allowing the comparison of xRatSLAM with OpenRatSLAM and assessing the impact of replacing framework components. The results provide evidence that the maps produced by xRatSLAM are similar to those produced by OpenRatSLAM when they are fed with the same input parameters, which is a positive result, and that implemented modules can be easily changed without impacting other parts of the framework