Fuzzy logic controlled miniature LEGO robot for undergraduate training system

Fuzzy logic enables designers to control complex systems more effectively than traditional approaches as it provides a simple way to arrive at a definite conclusion upon ambiguous, imprecise or noisy information. In this paper, we describe the development of two miniature LEGO robots, which are the line following and the light searching mobile robots to provide a better understanding of fuzzy logic control theory and real life application for an undergraduate training system. This study is divided into two parts. In the first part, an object sorter robot is built to perform pick and place task to load different colour objects on a fuzzy logic controlled line following robot which then carries the preloaded objects to a goal by following a white line. In the second part, an intelligent fuzzy logic controlled light searching robot with the capability to navigate in a maze is developed. All of the robots are constructed by using the LEGO Mindstorms kit. Interactive C programming language is used to program fuzzy logic robots. Experimental results show that the robots has successfully track the predefined path and navigate towards light source under the influence of the fuzzy logic controller; and therefore can be used as a training system in undergraduate fuzzy logic class

Factory Location Decision Making Based on the FUZZY Inference Model

This paper introduces the concept of fuzzy logic, some terms used in this kind of logic, and uses it to evaluate and choose where to deploy factories and other enterprises. In addition, a model is made using the InFuzzy program to evaluate a choice of a location within the Manaus Industrial Pole - PIM, using objective and subjective criteria within the fuzzy logic. This article aims to present the fuzzy logic in the context of production engineering, select the parameters that define the best location, develop models that represent the subject in the study and verify the applicability by simulating other case studies and comparing results

Fuzzy Logic in Collective Robotic Search

One important application of mobile robots is searching a geographical region to locate the origin of a specific sensible phenomenon. We first propose a fuzzy logic approach using a decision table. A novel fuzzy rule based was designed. And then a fuzzy search strategy is adopted by utilizing the three tier centers of mass coordination. Experimental results show that fuzzy logic algorithm is an efficient approach for the collective robots to locate the target source. In addition, noise and the position of the target affect the searching result

The response of fuzzy electronics to ionizing radiation

Small satellites such as CubeSats operate under environmental constraints that are outside of typical commercial specifications. Such constraints include the ability to operate over an extended temperature range and during exposure to ionizing radiation. Nevertheless, commercial technologies are being implemented in CubeSat spacecraft because of the low-cost, low-power, and space savings requirements often achievable with advanced microelectronics [1]. Due to flexibility and the ability to handle uncertainty, fuzzy logic is viable for satellite control while meeting the strict design requirements of a CubeSat. This work evaluates the response of fuzzy control logic to ionizing radiation and compares the response to that of conventional systems. Fuzzy logic operates on multiple truth values which vary within the range of 0 and 1, as opposed to Boolean logic’s precise, two-variable system. Fuzzy systems utilize “if-then” statements, known as membership functions. These allow for terms such as “moderately” or “slightly,” to be utilized, permitting flexibility within the system. As such, fuzzy logic shows promise in robotics and mechanical control systems due to the ability to handle uncertainty and non-linearity. Thus, fuzzy logic electronics are a candidate for small satellite control mechanisms, creating the potential for radiation hardened control systems that take advantage of the low-power and space savings achievable by modern electronics technologies. A common effect of ionizing radiation is single event effects (SEEs). SEEs generally result in erroneous transient behavior following the interaction of single ionizing particles with semiconductors. Little is known about the response of fuzzy logic systems to such effects. This work aims to evaluate the effects of SEE on a fuzzy logic small satellite attitude controller, describe the mechanisms of vulnerability, and compares the response to standard controller designs

A fuzzy logic application in virtual education

Traditionally, the teaching and learning process uses the problems resolving for fixing, transmitting and evaluating concepts and knowledge about a subject. Learning is the process of acquiring relative permanent changes in understanding, attitude, knowledge, information, capacity and ability through experience. A change can be decided or involuntary, to better or worsen learning. The learning process is an internal cognitive event. To help this teaching and learning process, it is important the use of a computer tool able to stimulate these changes. Also, it is important that it can function as validation and helping tool to the student. These functions are performed by computer systems called Intelligent Tutoring Systems. This paper describes the use of artificial intelligence techniques as a teaching support tool. Using Intelligent Tutoring Systems e fuzzy logic, this work shows, throgh eletronic ways, teaching will be more efficient and more adapted to students necessities, in group or individually

Simulation of Unified Power Quality Conditioner for Power Quality Improvement Using Fuzzy Logic and Neural Networks

One of the major concerns in electricity industry today is power quality. It becomes especially important with the introduction of advanced and complicated devices, whose performance is very sensitive to the quality of power supply. The electronic devices are very sensitive to disturbances and thus industrial loads become less tolerant to power quality problems such as voltage dips, voltage sags, voltage flickers, harmonics and load unbalance etc. At present, a wide range of very flexible controllers, which capitalize on newly available power electronics components, are emerging for custom power applications. Among these, the distribution static compensator, dynamic voltage restorer and unified power quality conditioner which is based on the VSC principle are used for power quality improvement. In this project, a fuzzy logic controller with reference signal generation method is designed for UPQC and compared its performance with artificial neural network based controller. This is used to compensate current and voltage quality problems of sensitive loads. The results are analyzed and presented using matlab/simulink software . Keywords: power quality, upqc, voltage sag, fuzzy logic controller, neural network

Implementation of genetic algorithm based fuzzy logic controller with automatic rule extraction in FPGA

A number of fuzzy logic controllers are being designed till now to replace complex, non-linear and huge controlling equipment in numerous industrial sectors. But the designing of these controllers requires thorough knowledge about the controlled process. For this purpose a highly experienced experts are required, which is not feasible all the time. Most of these processes are non-linear and depend on large number of parameters. Thus mathematical representation of these systems is an arduous line of work. This project addresses these problems by proposing using of genetic algorithm based Fuzzy Logic systems as controllers. The system includes algorithms which are run on a capable computing platform, to read an experimental data sheet obtained from experimental observations of the system and generate a fine tuned rule base that is to be used in the fuzzy logic controller hardware. The hardware is implemented in an FPGA. Transfer of synthesized rule base from the computer to the FPGA implementation and crisp output value back to the computer is done by UART. A graphical user interface is provided that runs on the computer

Design of Mamdani - Type Model for Predicting the Future Price of Fuel on the Basis of Demand and Supply

This paper presents the design of fuzzy inference system for predicting the price of the petroleum product on the basis of demand and supply. As the demand increases and the supply decreases the price of petroleum products also increases. Modeling of efficient price estimation system on the basis of two inputs as demand and supply using Mamdani model is presented in this paper. The inference engines are modeled using the FIS editor of Fuzzy Logic toolbox, a tool of Matlab. Out of various methods available, Center of gravity (CG) defuzzification method is used for obtaining the crisp output. It is proposed to consistently handle all linguistic derivations that allow “IF-THEN” formulation by applying Fuzzy Logic (FL). The parameters for the input variables and output variable and their membership functions works on the range of the values for demand and supply. The results obtained are analyzed to explore the design space. DOI: 10.17762/ijritcc2321-8169.15063