Ivan Rossato Chrun

Hybrid Dynamic Fuzzy Cognitive Maps and Hierarchical Fuzzy logic controllers for Autonomous Mobile Navigation

This work is an evolution of a previous work in which we used a knowledge-based system to autonomous navigation area. Two navigation systems are developed: one uses Fuzzy Cognitive Maps (FCM) and the other is based on a Fuzzy Logic Controller (FLC). For the first system, a variant of the classical FCM, named Hybrid-Dynamic Fuzzy Cognitive Maps (HDFCM), is used to model decision tasks and/or to make inference into a mobile navigation context. Fuzzy Cognitive Maps are a tool that model qualitative structured knowledge through concepts and causal relationships. The proposed model allows representing the dynamic behavior of a mobile robot in presence of changes in the environment. A Hierarchical Weighted Fuzzy Logic Controller (HW-FLC) composes the second navigation system. Simulation results are presented allowing a comparison among both systems and showing the ability of the mobile robot to navigate among obstacles in different scenarios (navigation environment). Finally, initial real results with Arduino micro-controller are showed.

Hybrid Dynamic Fuzzy Cognitive Maps Evolution for autonomous navigation system

This work develops a knowledge-based system to autonomous navigation using Fuzzy Cognitive Maps (FCM). A new variant of FCM, named Hybrid-Dynamic Fuzzy Cognitive Maps (HD-FCM), is used to model decision tasks and/or to make inference into a mobile navigation context. Fuzzy Cognitive Maps are a tool that model qualitative structured knowledge through concepts and causal relationships. The proposed model allows representing the dynamic behavior of a mobile robot in presence of environment changes. A brief review of correlated works in the navigation area, using FCM evolutions, is presented. Some simulation results are discussed and compared with Weighted Fuzzy System for shows the ability of the mobile to navigate among obstacles in different scenarios (navigation environment).

Artificial Life Environment Modeled by Dynamic Fuzzy Cognitive Maps

This paper presents an artificial life environment based on dynamic fuzzy cognitive maps (DFCMs) and inspired by multiagent systems, machine learning, and concepts from classical fuzzy cognitive map theory. The proposed architecture includes features such as a reinforcement learning algorithm to dynamically fine-tune the weights of the DFCM, a finite states machine, governing the behavior of the creatures by adding/removing concepts into the DFCM, and others. These features are used to add adaptability to the artificial creatures (agents) in a simulated hunter-prey environment with synthetic data. Some experiments carried out in a simulated virtual environment have shown promising results for further research in the subject of this paper.

Embedded Dynamic Fuzzy Cognitive Maps for Controller in Industrial Mixer

This paper presents the application of certain intelligent techniques to control an industrial mixer. Control design is based on Hebbian modification of Fuzzy Cognitive Maps learning. This research study develops a Dynamic Fuzzy Cognitive Map (DFCM) based on Hebbian Learning algorithms. It was used Fuzzy Classic Controller to help validate simulation results of an industrial mixer of DFCM. Experimental analysis of simulations in this control problem was conducted. Additionally, the results were embedded using efficient algorithms into the Arduino platform in order to acknowledge the performance of the codes reported in this paper.

Sistema Supervisório e Controle Multivariável aplicando Controlador Fuzzy Ponderado-PID em um Processo de Fermentação Alcoólica

In this work, it is analyzed a multivariate system control of an alcoholic fermentation process with no minimum phase. The control is made with PID classic controllers associated with a supervisory system based on Fuzzy Systems. The Fuzzy system, a priori, send set-points to PID controllers, but also adds protection functions, such as if the biomass valued is at zero or very close. The Fuzzy controller changes the campaign to prevent or mitigate the paralyzation of the process. Three control architectures based on Fuzzy Control Systems are presented and compared in performance with classic control in different campaigns. The third architecture, in particular, adds an adaptive function. A brief summary of Fuzzy theory and correlated works will be presented. And, finally simulations results, conclusions and future works end the article.

Fuzzy Cognitive Maps Applied to Student Satisfaction Level in an University

The project aims to develop an FCM (Fuzzy Cognitive Map) whose function is to assess the quality level of students at Federal Technological University of Parana, Campus Cornélio Procópio (UTFPR-CP). The FCM combines aspects of other intelligent techniques. This tool has inference capacity through concepts and causal relationships between them (the level of influence between the variables of the model). Its development begins with the determination of the possible areas that would affect, or would fit as indicators for “Level of Satisfaction” in UTFPR-CP. Through online forms, it was possible to quantify the influence of following initially detected areas: teacher training, structures of laboratories and class rooms, housing, library and cleaning. In general, education institutions do not have tools that provide a critical analysis of its quality. This work aims to propose a tool for improving the institution in a medium/long term. So that: with the development of the FCM model, it was possible to identify the positive and negative points that affecting the level of satisfaction in UTFPR-CP.

Sistema de Navegação Autônoma inspirado em Robótica de Enxame

Resumo—Neste trabalho é apresentada uma estratégia de navegação em bando baseada em técnicas de Inteligência Coletiva para o controle de robôs autônomos utilizando Redes Cognitivas Dinâmicas Simplificadas (Simplified Dynamic Cognitive Networks, sDCN), uma evolução dos Mapas Cognitivos Fuzzy (Fuzzy Cognitive Maps, FCM). A construção e fundamentos dos controladores sDCNs serão apresentados no desenvolvimento desse trabalho. E, finalmente, resultados simulados utilizando um ambiente gráfico com duas dimensões em três diferentes cenários para observar resultados dos múltiplos objetivos: navegação em formação (seguindo o líder do grupo) e desvio de obstáculos.

Autonomous Navigation Applying Dynamic-Fuzzy Cognitive Maps and Fuzzy Logic

This work develops a knowledge based system to autonomous navigation using Fuzzy Cognitive Maps (FCM). A new variant of FCM, named Dynamic-Fuzzy Cognitive Maps (D-FCM), is used to model decision tasks and/or make inference in the robot or mobile navigation. Fuzzy Cognitive Maps is a tool that model qualitative structured knowledge through concepts and causal relationships. The proposed model allows representing the dynamic behavior of the mobile robot in presence of environment changes. A brief review of correlated works in the navigation area by use of FCM is presented. Some simulation results are discussed highlighting the ability of the mobile to navigate among obstacle (navigation environment). A comparative with Fuzzy Logic and future works are also addressed.