scholarly journals Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant

2022 ◽  
Vol 12 (2) ◽  
pp. 541
Author(s):  
Helbert Espitia ◽  
Iván Machón ◽  
Hilario López
Keyword(s):  
Control System ◽  
Design Process ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Fuzzy Controller ◽  
Initial Configuration ◽  
Control Action ◽  
Linear Controller ◽  
Neuro Fuzzy ◽  
And Control

One characteristic of neuro-fuzzy systems is the possibility of incorporating preliminary information in their structure as well as being able to establish an initial configuration to carry out the training. In this regard, the strategy to establish the configuration of the fuzzy system is a relevant aspect. This document displays the design and implementation of a neuro-fuzzy controller based on Boolean relations to regulate the angular position in an electromechanical plant, composed by a motor coupled to inertia with friction (a widely studied plant that serves to show the control system design process). The structure of fuzzy systems based on Boolean relations considers the operation of sensors and actuators present in the control system. In this way, the initial configuration of fuzzy controller can be determined. In order to perform the optimization of the neuro-fuzzy controller, the continuous plant model is converted to discrete time to be included in the closed-loop controller training equations. For the design process, first the optimization of a Proportional Integral (PI) linear controller is carried out. Thus, linear controller parameters are employed to establish the structure and initial configuration of the neuro-fuzzy controller. The optimization process also includes weighting factors for error and control action in such a way that allows having different system responses. Considering the structure of the control system, the optimization algorithm (training algorithm) employed is dynamic back propagation. The results via simulations show that optimization is achieved in the linear and neuro-fuzzy controllers using different weighting values for the error signal and control action. It is also observed that the proposed control strategy allows disturbance rejection.

scholarly journals Compact Fuzzy Systems Based on Boolean Relations

2021 ◽  
Vol 11 (4) ◽  
pp. 1793
Author(s):  
Helbert Espitia ◽  
José Soriano ◽  
Iván Machón ◽  
Hilario López
Keyword(s):  
Design Process ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Hydraulic System ◽  
Design Methodology ◽  
Satisfactory Performance ◽  
And Control ◽  
Equivalent Expressions ◽  
Nonlinear Plant

This document presents some considerations and procedures to design a compact fuzzy system based on Boolean relations. In the design process, a Boolean codification of two elements is extended to a Kleene’s of three elements to perform simplifications for obtaining a compact fuzzy system. The design methodology employed a set of considerations producing equivalent expressions when using Boole and Kleene algebras establishing cases where simplification can be carried out, thus obtaining compact forms. In addition, the development of two compact fuzzy systems based on Boolean relations is shown, presenting its application for the identification of a nonlinear plant and the control of a hydraulic system where it can be seen that compact structures describes satisfactory performance for both identification and control when using algorithms for optimizing the parameters of the compact fuzzy systems. Finally, the applications where compact fuzzy systems are based on Boolean relationships are discussed allowing the observation of other scenarios where these structures can be used.

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

MODELING THE SYNTHESIS OF TAKAGI — SUGENO — KANG FUZZY CONTROLLERS IN SOME CONTROL SYSTEMS

2021 ◽  
Vol 7 (2) ◽  
pp. 147-169
Author(s):  
Irina V. Kulikova
Keyword(s):  
Genetic Algorithms ◽  
Control System ◽  
Fuzzy Controller ◽  
Industrial Society ◽  
Control Action ◽  
Effective Control ◽  
Control Parameters ◽  
Membership Functions ◽  
Fuzzy Controllers ◽  
Takagi Sugeno

Modern challenges in a post-industrial society require further development of management systems for complex technical and technological phenomena and processes. Effective control of an object is possible if a controller, or a fuzzy controller, correctly generates the required control action. Recently, fuzzy controllers have been very popular. Fuzzy logical statements in this case help considering various nonlinear relationships. The synthesis of the fuzzy controller parameters allows for more efficient operation of the control system. A possible option for obtaining the best set of parameters for a fuzzy controller is the use of genetic algorithms for its synthesis. The use of genetic algorithms for the fuzzy controllers synthesis can lead to the fact that the elements of its parameters array will change in such a way that an incorrect value of one or more elements will occur. This situation leads to impossibility of composing membership functions for the terms of the variables of the fuzzy controller. Incorrect value formation is excluded by constructing a limited functional dependency. This paper proposes a mathematical model of the parameters of the term-set of variables of a fuzzy controller of the Takagi — Sugeno — Kang type of the zero and first orders. The authors disclose the content of the conditions and conclusions of the rule base for the fuzzy controller of the above type. As a result of the simulation, some operations of the genetic algorithm are implemented using a random number generator. Graphical models of the membership functions of the input variables of the fuzzy controller of the type under consideration clearly illustrate the occurrence of all parameters in their range of possible values. A description of the control system operation with two control parameters and one control action at the specified values of the control parameters is presented.

Neuro-Fuzzy System Modeling

2010 ◽  
pp. 147-175
Author(s):  
Chen-Sen Ouyang
Keyword(s):  
Neural Networks ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
System Modeling ◽  
Fuzzy Modeling ◽  
Structure Identification ◽  
Learning Capability ◽  
Computing Paradigm ◽  
Emerging Trends ◽  
Neuro Fuzzy

Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known modeling approaches of neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability. Up to now, many approaches have been proposed for neuro-fuzzy modeling. However, it still exists many problems need to be solved. In this chapter, the authors firstly give an introduction to neuro-fuzzy system modeling. Secondly, some basic concepts of neural networks, fuzzy systems, and neuro-fuzzy systems are introduced. Also, they review and discuss some important literatures about neuro-fuzzy modeling. Thirdly, the issue for solving two most important problems of neuro-fuzzy modeling is considered, i.e., structure identification and parameter identification. Therefore, the authors present two approaches to solve these two problems, respectively. Fourthly, the future and emerging trends of neuro-fuzzy modeling is discussed. Besides, the possible research issues about neuro-fuzzy modeling are suggested. Finally, the authors give a conclusion.

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.

Modeling DCS of Nuclear Power Plant With Extended GO-FLOW Methodology

2017 ◽  
Author(s):  
Lu Hongxing ◽  
Yang Ming ◽  
Dai Xinyu ◽  
Li Wei ◽  
Yoshikawa Hidekazu
Keyword(s):  
Control System ◽  
Flow Model ◽  
Modeling Method ◽  
System Structure ◽  
Control Action ◽  
Volume Control ◽  
Flow Modeling ◽  
Control Logic ◽  
And Control ◽  
The Relationship

GO-FLOW model is a success-oriented system modeling method which describes how the system configures its resources to achieve required functions by using basic functional units in terms of substances and demand flows in the system. The GO-FLOW models, which are directly built according to system structure drawings, can be used to analyze the reliability of a system with time and phased mission problems. With the development of Digital Control System (DCS), the reliability analysis of whole DCS has become an important task. However, there are some shortcomings using the traditional GO-FLOW methodology to model DCS: 1. There are not abundant operators in the GO-FLOW model to describe the control logic in DCS; 2. It is hard to model the relationship between the control actions and hardware devices using traditional GO-FLOW methodology; This paper presents an extended GO-FLOW modeling method. In this study, the GO-FLOW model is supplemented and improved, which can accurately describe the relationship and control logic between the hardware and control action (or human control action) in the running process of the DCS. In this paper, taking the Chemical and Volume Control System (CVCS) as an example, using the extended GO-FLOW modeling method established the model of CVCS, and the model of DCS control logic. This improved modeling method can be applied to the reliability modeling and evaluation of DCS.

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