Hardware Implementation of a Neuro-Fuzzy Controller Using High Level Synthesis Tool

MACRo 2015 ◽  
2015 ◽  
Vol 1 (1) ◽  
pp. 183-191 ◽  
Author(s):  
Tibor Tămas ◽  
Sándor Tihamér Brassai
Keyword(s):  
Embedded System ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Design Flow ◽  
High Level Synthesis ◽  
Ip Core ◽  
Inference System ◽  
System Architectures ◽  
Neuro Fuzzy ◽  
High Level

AbstractThe purpose of this work is to present the design flow and the implementation of a neuro-fuzzy controller Intellectual Property (IP) core, using High Level Synthesis (HLS) tool. The realized IP core is designed for FPGA based embedded system architectures. The implemented control algorithm is a Sugeno model based Adaptive Neuro-Fuzzy Inference System (ANFIS). The optimization possibilities using the HLS tool and the designing of the interfaces for the IP core are presented.

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).

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 ANFIS control of a shunt active filter based with a five-level NPC inverter to improve power quality

2021 ◽  
Vol 11 (3) ◽  
pp. 1886
Author(s):  
Mahmoud Mostefa Tounsi ◽  
Ahmed Allali ◽  
Houari Merabet Boulouiha ◽  
Mouloud Denaï
Keyword(s):  
Power Quality ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Harmonic Distortion ◽  
Active Filter ◽  
Nonlinear Loads ◽  
Inference System ◽  
Shunt Active Filter ◽  
Neuro Fuzzy

This paper addresses the problem of power quality, and the degradation of the current waveform in the distribution network which results directly from the proliferation of the nonlinear loads. We propose to use a five-level neutral point clamped (NPC) inverter topology for the implementation of the shunt active filter (SAPF). The aim of the SAPF is to inject harmonic currents in phase opposition at the connection point. The identification of harmonics is based on the pq method. A neuro-fuzzy controller based on ANFIS (adaptive neuro fuzzy inference system) is designed for the SAPF. The simulation study is carried out using MATLAB/Simulink and the results show a significant improvement in the quality of energy and a reduction in total harmonic distortion (THD) in accordance with IEC standard, IEEE-519, IEC 61000, EN 50160.

scholarly journals Design of Neuro-Fuzzy Controller for Stabilization of Single Phase Stand-Alone PV Power System

2018 ◽  
Vol 7 (3) ◽  
pp. 176
Author(s):  
Hitendra Singh Thakur ◽  
Ram Narayan Patel
Keyword(s):  
Power System ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Phase System ◽  
Inference System ◽  
Current Harmonics ◽  
Synchronous Reference Frame ◽  
Neuro Fuzzy ◽  
Three Phase

For the three phase power electronic and drive applications, vector control or the synchronous reference frame (SRF) based control concept is well accepted and settled amongst the research communities. Although the SRF concept has gained popularity and appreciation in developing the three phase controllers, still the concept has not reached the same level in case of a single phase system. The work presented in this paper is mainly concerned to the design of a hybrid Artificial Neural Network and Fuzzy Logic based controller for a single phase stand-alone photo-voltaic (PV) power system. The adaptive neuro fuzzy inference system (ANFIS) controller proposed in this paper is chiefly meant for improving the transient and steady state responses; for minimizing the distorting effect of the low order load current harmonics encountered particularly in case of switching the drive based inductive loads and to help maintain the inverter output voltage constant under different loading circumstances. The result obtained through simulation work, shows the effectiveness of the proposed controller as compared with the previously established research works.

Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle Control of Inverted Pendulum (IP) Systems

Fuzzy Systems ◽  
2017 ◽  
pp. 308-320
Author(s):  
Ashwani Kharola
Keyword(s):  
Fuzzy Logic ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Data Sets ◽  
Inference System ◽  
Simulink Model ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper illustrates a comparison study of Fuzzy and ANFIS Controller for Inverted Pendulum systems. IP belongs to a class of highly non-linear, unstable and multi-variable systems which act as a testing bed for many complex systems. Initially, a Matlab-Simulink model of IP system was proposed. Secondly, a Fuzzy logic controller was designed using Mamdani inference system for control of proposed model. The data sets from fuzzy controller was used for development of a Hybrid Sugeno ANFIS controller. The results shows that ANFIS controller provides better results in terms of Performance parameters including Settling time(sec), maximum overshoot(degree) and steady state error.

scholarly journals Emotional Video to Audio Transformation Using Deep Recurrent Neural Networks and a Neuro-Fuzzy System

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Gwenaelle Cunha Sergio ◽  
Minho Lee
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Audio Signals ◽  
Global Features ◽  
Inference System ◽  
Audio Features ◽  
Neuro Fuzzy ◽  
High Level ◽  
Domain Transformation

Generating music with emotion similar to that of an input video is a very relevant issue nowadays. Video content creators and automatic movie directors benefit from maintaining their viewers engaged, which can be facilitated by producing novel material eliciting stronger emotions in them. Moreover, there is currently a demand for more empathetic computers to aid humans in applications such as augmenting the perception ability of visually- and/or hearing-impaired people. Current approaches overlook the video’s emotional characteristics in the music generation step, only consider static images instead of videos, are unable to generate novel music, and require a high level of human effort and skills. In this study, we propose a novel hybrid deep neural network that uses an Adaptive Neuro-Fuzzy Inference System to predict a video’s emotion from its visual features and a deep Long Short-Term Memory Recurrent Neural Network to generate its corresponding audio signals with similar emotional inkling. The former is able to appropriately model emotions due to its fuzzy properties, and the latter is able to model data with dynamic time properties well due to the availability of the previous hidden state information. The novelty of our proposed method lies in the extraction of visual emotional features in order to transform them into audio signals with corresponding emotional aspects for users. Quantitative experiments show low mean absolute errors of 0.217 and 0.255 in the Lindsey and DEAP datasets, respectively, and similar global features in the spectrograms. This indicates that our model is able to appropriately perform domain transformation between visual and audio features. Based on experimental results, our model can effectively generate an audio that matches the scene eliciting a similar emotion from the viewer in both datasets, and music generated by our model is also chosen more often (code available online at https://github.com/gcunhase/Emotional-Video-to-Audio-with-ANFIS-DeepRNN).

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