scholarly journals Comparison of self-tuned Neuro-Fuzzy controllers on 2 DOF helicopter: an application

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Muhammet Öztürk ◽  
İbrahim Özkol
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Strong Stability ◽  
Inference System ◽  
New Approach ◽  
Fuzzy Controllers ◽  
Advantages And Disadvantages ◽  
Neuro Fuzzy ◽  
On Line ◽  
The Difference

AbstractIn this paper, a new approach for Neuro-Fuzzy Controller (NFC) has been presented and compared to previously defined NFCs given in open literature. The proposed controller is based on an on-line Adaptive Neuro-Fuzzy Inference System (ANFIS) and meticulous analysis through simulations is performed to show its robustness. The performance of Neuro-Fuzzy Controllers (NFC) depends on controller inputs. To show the difference and superiority of the proposed controller, many studies in the open literature are examined and compared. Therefore, the advantages and disadvantages of the Neuro-Fuzzy controller are outlined and an optimum Neuro-Fuzzy controller is structured and presented. To test our developed controller for a nonlinear problem, having coupling effects, a 2 DOF helicopter model is chosen. Also to show the robustness, the controller performance which is applied to a 2 DOF helicopter is investigated and compared with other Neuro-Fuzzy controller structures. To better show NFC performance, NFC control results were compared with LQR+I. It is observed that besides being on-line adaptive for all systems, the controller developed has many priorities such as noiseless, strong stability, and better response time.

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 Adaptive neuro-fuzzy controller of switched reluctance motor

2007 ◽  
Vol 4 (1) ◽  
pp. 23-34 ◽  
Author(s):  
Ahmed Tahour ◽  
Hamza Abid ◽  
Ghani Aissaoui
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Digital Simulation ◽  
Switched Reluctance Motor ◽  
Motor Speed ◽  
Inference System ◽  
Switched Reluctance ◽  
Neuro Fuzzy ◽  
Reluctance Motor

This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI).

An Artificial Intelligence Based Approach for High Impedance Faults Analysis in Distribution Networks

2012 ◽  
Vol 1 (2) ◽  
pp. 44-59 ◽  
Author(s):  
M. S. Abdel Aziz ◽  
M. A. Moustafa Hassan ◽  
E. A. El-Zahab
Keyword(s):  
Artificial Intelligence ◽  
Distribution System ◽  
Fault Location ◽  
Fuzzy Inference ◽  
Distribution Networks ◽  
Inference System ◽  
New Approach ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Faults

This paper presents a new approach for high impedance faults analysis (detection, classification and location) in distribution networks using Adaptive Neuro Fuzzy Inference System. The proposed scheme was trained by data from simulation of a distribution system under various faults conditions and tested for different system conditions. Details of the design process and the results of performance using the proposed method are discussed. The results show the proposed technique effectiveness in detecting, classifying, and locating high impedance faults. The 3rd harmonics, magnitude and angle, for the 3 phase currents give superior results for fault detection as well as for fault location in High Impedance faults. The fundamental components magnitude and angle for the 3 phase currents give superior results for classification phase of High Impedance faults over other types of data inputs.

A new approach to estimate anthropometric measurements by adaptive neuro-fuzzy inference system

2003 ◽  
Vol 32 (2) ◽  
pp. 105-114 ◽  
Author(s):  
M. Dursun Kaya ◽  
A. Samet Hasiloglu ◽  
Mahmut Bayramoglu ◽  
Hakki Yesilyurt ◽  
A. Fahri Ozok
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Anthropometric Measurements ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy

scholarly journals Expert System Models for Forecasting Forklifts Engagement in a Warehouse Loading Operation: A Case Study

2016 ◽  
Vol 28 (4) ◽  
pp. 393-401 ◽  
Author(s):  
Dejan Mirčetić ◽  
Nebojša Ralević ◽  
Svetlana Nikoličić ◽  
Marinko Maslarić ◽  
Đurđica Stojanović
Keyword(s):  
Expert System ◽  
Customer Service ◽  
Fuzzy Inference ◽  
Service Level ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy ◽  
Customer Service Level ◽  
Distribution Chain

The paper focuses on the problem of forklifts engagement in warehouse loading operations. Two expert system (ES) models are created using several machine learning (ML) models. Models try to mimic expert decisions while determining the forklifts engagement in the loading operation. Different ML models are evaluated and adaptive neuro fuzzy inference system (ANFIS) and classification and regression trees (CART) are chosen as the ones which have shown best results for the research purpose. As a case study, a central warehouse of a beverage company was used. In a beverage distribution chain, the proper engagement of forklifts in a loading operation is crucial for maintaining the defined customer service level. The created ES models represent a new approach for the rationalization of the forklifts usage, particularly for solving the problem of the forklifts engagement incargo loading. They are simple, easy to understand, reliable, and practically applicable tool for deciding on the engagement of the forklifts in a loading operation.

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