scholarly journals DESIGN AND MODELING OF ANFIS REGULATOR TO COMPENSATE DEVIATIONS OF MOBILE PLATFORM OF PARALLEL CABLE-DRIVEN ROBOT

2021 ◽  
pp. 42-48
Author(s):  
E. A. Marchuk ◽  
A. V. Maloletov
Keyword(s):  
Control System ◽  
Quality Criterion ◽  
Mobile Platform ◽  
Neuro Fuzzy ◽  
Fuzzy Regulator

The article deals with a description of design and modeling of neuro-fuzzy regulator (ANFIS) which provides a compensation of deviations of mobile platform of parallel cable-driven robot. The regulator uses adjusting of lengths of unwounded cables. Evaluation of the control system has been made according to the quality criterion as an elongation of each cable comparatively to given values.

scholarly journals An Adaptive Neuro-Fuzzy Control of Pneumatic Mechanical Ventilator

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 51
Author(s):  
Jozef Živčák ◽  
Michal Kelemen ◽  
Ivan Virgala ◽  
Peter Marcinko ◽  
Peter Tuleja ◽  
...  
Keyword(s):  
Control System ◽  
Control Algorithm ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Mechanical Design ◽  
Mechanical Ventilator ◽  
Mechanical Ventilators ◽  
Inference System ◽  
Control Approach ◽  
Neuro Fuzzy

COVID-19 was first identified in December 2019 in Wuhan, China. It mainly affects the respiratory system and can lead to the death of the patient. The motivation for this study was the current pandemic situation and general deficiency of emergency mechanical ventilators. The paper presents the development of a mechanical ventilator and its control algorithm. The main feature of the developed mechanical ventilator is AmbuBag compressed by a pneumatic actuator. The control algorithm is based on an adaptive neuro-fuzzy inference system (ANFIS), which integrates both neural networks and fuzzy logic principles. Mechanical design and hardware design are presented in the paper. Subsequently, there is a description of the process of data collecting and training of the fuzzy controller. The paper also presents a simulation model for verification of the designed control approach. The experimental results provide the verification of the designed control system. The novelty of the paper is, on the one hand, an implementation of the ANFIS controller for AmbuBag pressure control, with a description of training process. On other hand, the paper presents a novel design of a mechanical ventilator, with a detailed description of the hardware and control system. The last contribution of the paper lies in the mathematical and experimental description of AmbuBag for ventilation purposes.

scholarly journals Neuro-Fuzzy Control of a Robotic Manipulator

2014 ◽  
Vol 19 (3) ◽  
pp. 575-584 ◽  
Author(s):  
P. Gierlak ◽  
M. Muszyńska ◽  
W. Żylski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Intelligent Control ◽  
Fuzzy System ◽  
Operating Conditions ◽  
Intelligent Control System ◽  
Hybrid Controller ◽  
Compensatory Control ◽  
Neuro Fuzzy

Abstract In this paper, to solve the problem of control of a robotic manipulator’s movement with holonomical constraints, an intelligent control system was used. This system is understood as a hybrid controller, being a combination of fuzzy logic and an artificial neural network. The purpose of the neuro-fuzzy system is the approximation of the nonlinearity of the robotic manipulator’s dynamic to generate a compensatory control. The control system is designed in such a way as to permit modification of its properties under different operating conditions of the two-link manipulator

scholarly journals The Application of a Neuro-Fuzzy Adaptive Crane Control System

2010 ◽  
Vol 14-15 (1) ◽  
pp. 247-258
Author(s):  
Jarosław Smoczek ◽  
Janusz Szpytko
Keyword(s):  
Control System ◽  
Material Handling ◽  
Fuzzy Controller ◽  
Laboratory Model ◽  
Crane Control ◽  
Neuro Fuzzy ◽  
To Come ◽  
Fuzzy Control Algorithm ◽  
Fuzzy Adaptive

The Application of a Neuro-Fuzzy Adaptive Crane Control SystemThe unconventional methods, mostly based on fuzzy logic, are often addressed to a problem of anti-sway crane control. The problem of practical application of those solutions is important owing to come the growing expectations for time and precision of transportation operations and exploitation quality of material handling devices. The paper presents the designing methods of an adaptive anti-sway crane control system based on the neuro-fuzzy controller, as well as the software and hardware equipments used to aid the programming realization the fuzzy control algorithm on a programmable logic controller (PLC). The proposed application of control system was tested on the laboratory model of an overhead traveling crane.

Collision Avoidance System Synthesis for a Group of Robots in Unsupervised Learning Paradigm

2020 ◽  
Vol 21 (7) ◽  
pp. 420-427
Author(s):  
A. V. Dotsenko
Keyword(s):  
Control System ◽  
Collision Avoidance ◽  
Nearest Neighbor ◽  
Control Function ◽  
Quality Criterion ◽  
Test Sample ◽  
Evolutionary Strategies ◽  
Joint Control ◽  
Current State ◽  
And Control

Collision avoidance is very important problem in the domain of multi-robot interaction. In this paper we propose a new approach of collision avoidance in the context of the optimal control system synthesis problem definition with minimal information available. It is assumed that robots have a certain scope within which they can interact with static and dynamic phase constraints. A group of robots is considered to be homogeneous, and control system unit for reaching terminal states already available to robots. The control system which is responsible for collision avoidance is only activated when the nearest neighbor is located in the scope of the considered robot. The first important feature of this work is the fact that the collision avoidance between two robots is reciprocal with joint control system, without assigning priorities. Another key feature of this work is the complete absence of information about the environment and the current state of other robots at given time. Robots only share information with nearest neighbors if they locate in the scope of each other. We also present a computational experiment with mobile robots as control objects. A multilayer perceptron was used to approximate the control function. Weights of the perceptron were optimized in unsupervised paradigm by an algorithm belonging to the evolutionary strategies class. At the beginning of each epoch we generate a sample of collision scenarios for optimization, while the quality criterion of the achieved weights at the end of epoch is evaluated on a fixed test sample. Experimental results demonstrate strong ability of the optimized multilayer perceptron to map the relative state of two mobile robots to controls in order to avoid collisions.

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