Fuzzy control system of robot angular attitude

Bobyr, M. V.; Kulabukhov, S. A.; Milostnaya, N. A.

doi:10.1109/icieam.2016.7910970

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…Other authors have presented algorithms for steering control [ 28 , 29 ]. For localization, using different fuzzy controllers and localization algorithms has been proposed in [ 30 , 31 , 32 , 33 ]. The applications proposed by us in this article are in a way of a novelty character, especially fuzzy control applications for estimating the dynamics of the pulpation in panic conditions and designing a Mamdani type fuzyy controller with two outputs.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Dumitrescu

Ciotîrnae

Vizitiu

2021

Sensors

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When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

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Section: Related Workmentioning

confidence: 99%

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Dumitrescu

Ciotîrnae

Vizitiu

2021

Sensors

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“…El trabajo realizado en [1] trata de un sistema de control de navegación parecido al propuesto en este artículo, en el que se usa la lógica difusa y en donde se observa que la implementación del sistema es bastante sencilla, obteniendo buenos resultados en la simulación numérica del sistema. Un sistema de evasión de obstáculos que hace uso de la lógica difusa es [6], el artículoúnicamente trata el problema de la evasión de obstáculos, por lo anterior no se implementa ninguna forma de navegación para llegar a un objetivo. En [4] se propone un sistema en chip que usa lógica difusa para navegación, este sistema está montado en un FPGA, por lo que aumenta la velocidad de procesamiento del sistema de control.…”

Section: Estado Del Arteunclassified

“…En los trabajos anteriores, la mayoría de los sistemas requieren de un sistema externo para montar el control difuso que se comunica con el robot, aprovechando así la facilidad de programación y lo recursos computacionales de los que podría carecer el robot móvil que se controla. A pesar de lo anterior, hay ejemplos en [6], [8], [5], en los cuales se han implementado controles difusos sencillos en placas Arduino las cuales carecen de los recursos de una computadora; pero que se podrían conectar al sistema a controlar mediante WiFi. Tomando como base lo anteriormente dicho, en este trabajo, se usará también una placa Arduino para la implementación del sistema de control difuso propuesto.…”

Section: Estado Del Arteunclassified

Sistema de navegación y evasión de obstáculos aplicando un sistema de control difuso en una placa Arduino UNO

Cristino¹,

Molina-Lozano²,

Espino³

2019

RCS

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Resumen. La navegación autónoma de robots móviles se ha convertido en unárea de grán interés, debido a su presencia en diversasáreas industriales. En este trabajo se propone la implementación de un sistema de control difuso tipo Mamdani que utiliza la defuzificación del Método del Centro de Máximos (COM), en una plataforma con recursos computacionales limitados (arduino UNO); demostrando así, que es posible implementar controles difusos en sistemas de poco poder computacional sin tener que implementar un sistema de comunicación a una plataforma con mayores recursos (computadora).Palabras clave: conjuntos difusos, lógica difusa, control difuso, robot diferencial, navegación autónoma.Abstract. Autonomous navigation of mobile robots has become an area of great interest, due to its presence in various industrial areas. This paper proposes the implementation of a Mamdani type fuzzy control system that uses the defuzzification of the Maximum Center Method (COM), on a platform with limited computational resources (arduino UNO); thus demonstrating that it is possible to implement fuzzy controls in systems of little computing power without having to implement a communication system to a platform with greater resources (computer).

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“…Cutting speed (V), the adjustable delay (t) Rotational speed of the drill (n) The defuzzification was carried out on the base of the method center of gravity [8,9,10] ( )…”

Section: Miso-systemmentioning

confidence: 99%