Context-based Reasoning through Fuzzy Logic for Edge Intelligence

2021 ◽  
Vol 15 (01) ◽  
pp. 17-25
Author(s):  
Ramin Firouzi ◽  
Rahim Rahmani ◽  
Theo Kanter
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Edge Computing ◽  
Learning System ◽  
Process Data ◽  
Real Time Processing ◽  
Low Level ◽  
Context Reasoning ◽  
Novel Approach ◽  
Rich Data

With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that are physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.

scholarly journals Artificial neural network model and fuzzy logic control of dissolved oxygen in a bioreactor

2020 ◽  
Vol 17 (3) ◽  
pp. 1289
Author(s):  
Nor Hana Mamat ◽  
Samsul Bahari Mohd Noor ◽  
Laxshan A/L Ramar ◽  
Azura Che Soh ◽  
Farah Saleena Taip ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dissolved Oxygen ◽  
Network Model ◽  
Neural Network Model ◽  
Fermentation Process ◽  
Fuzzy Logic Controller ◽  
Rbf Neural Network ◽  
Aeration Rate ◽  
Process Data

In a fermentation process, dissolved oxygen is the one of the key process variables that needs to be controlled because of the effect they have on the product quality. In a penicillin production, dissolved oxygen concentration influenced biomass concentration. In this paper, multilayer perceptron neural network (MLP) and Radial Basis Function (RBF) neural network is used in modeling penicillin fermentation process. Process data from an industrial scale fed-batch bioreactor is used in developing the models with dissolved oxygen and penicillin concentration as the outputs. RBF neural network model gives better accuracy than MLP neural network. The model is further used in fuzzy logic controller design to simulate control of dissolved oxygen by manipulation of aeration rate.  Simulation result shows that the fuzzy logic controller can control the dissolved oxygen based on the given profile.

Control of a Projectile Smart Fin Using an Inverse Dynamics-Based Fuzzy Logic Controller

2007 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Woosoon Yim ◽  
Paul Weinacht ◽  
Venkat Mudupu
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Inverse Dynamics ◽  
Operating Conditions ◽  
Attractive Alternative ◽  
Fuzzy Logic Controllers ◽  
Novel Approach ◽  
External Moment ◽  
Element Approach ◽  
Simulation Results

The objective of this paper is to explore a method for the design of fuzzy logic controller for a smart fin used to control the pitch and yaw attitudes of a subsonic projectile during flight. Piezoelectric actuators are an attractive alternative to hydraulic actuators commonly used in this application due to their simplicity. The proposed cantilever-shaped actuator can be fully enclosed within the hollow fin with one end fixed to the rotation axle of the fin while the other end is pinned at the trailing edge of the fin. The paper includes a dynamic model of the system based on the finite element approach. The model includes external moment due to aerodynamic effects. This paper presents a novel approach for automatically creating fuzzy logic controllers for the fin. This approach uses the inverse dynamics of the smart fin system to determine the ranges of the variables of the controllers. Simulation results show that the proposed controller can successfully drive smart fin under various operating conditions.

A Single Phase Anti-Swing Fuzzy Logic Controller for an Overhead Crane With Hoisting

2006 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Jamil M. Renno ◽  
Kamal A. F. Moustafa
Keyword(s):  
Fuzzy Logic ◽  
Single Phase ◽  
Fuzzy Logic Controller ◽  
Inverse Dynamics ◽  
Operating Conditions ◽  
Overhead Crane ◽  
Fuzzy Logic Controllers ◽  
Overhead Cranes ◽  
Novel Approach ◽  
Logic Controllers

This paper presents a novel approach for automatically creating anti-swing fuzzy logic controllers for overhead cranes with hoisting. This approach uses the inverse dynamics of the overhead crane to determine the ranges of the variables of the controllers. The control action is distributed among three fuzzy logic controllers (FLCs): travel controller, hoist controller, and anti-swing controller. Simulation examples show that the proposed controller can successfully drive overhead cranes under various operating conditions.

Control of Stand-off-Distance in Abrasive Jet Machining - A Fuzzy Approach

2014 ◽  
Vol 592-594 ◽  
pp. 106-111 ◽  
Author(s):  
K. Saran Kumar ◽  
S. Karthick
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Removal Rate ◽  
Optimal Level ◽  
Machining Processes ◽  
Abrasive Jet Machining ◽  
Novel Approach ◽  
Micro Holes ◽  
Interval Type

In general Unconventional Machining Processes (UCMs) or Non Traditional Machining Processes (NTMs) are used only when no other Traditional Machining Processes can meet the necessary requirements, both efficiently and economically. This is because using of most of NTMs incurs relatively higher installation, maintenance, operating and tooling costs. Now-a-days, complicated and intricate shaped structures and drilling of square and micro holes are done using the NTMs. Among the several NTMs available, Abrasive Jet Machining (AJM) is one widely used technique. There are several Process Parameters involved in this process and also have a greater impact on the overall machining performances (i.e.) Material removal Rate (MRR). In this paper a novel approach is made to control the Stand-off-Distance (SOD) at an optimal level to achieve higher MRR using Fuzzy Logic. The Fuzzy controller technique such as Type 1 Fuzzy Logic Controller and Interval Type 2 Fuzzy Logic Controller are compared which tends to control the servo mechanism that actuates the nozzle to maintain the altitude between nozzle tip and workpiece. This experimentation will serve the purpose of handling materials with non-uniform surfaces in them.

A Novel Approach to GSA, GA and Wavelet Transform to Design Fuzzy Logic Controller for 1ϕ Multilevel Inverter

2016 ◽  
Vol 7 (4) ◽  
pp. 1200
Author(s):  
Varsha Singh ◽  
S. Gupta ◽  
S. Pattnaik ◽  
Aarti Goyal
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Output Voltage ◽  
Closed Loop ◽  
Fuzzy Logic Controller ◽  
Multilevel Inverter ◽  
Closed Loop Control ◽  
Voltage Waveform ◽  
Loop Control ◽  
Novel Approach

<p>This paper proposes a novel approach for obtaining a closed loop control scheme based on Fuzzy Logic Controller to regulate the output voltage waveform of multilevel inverter. Fuzzy Logic Controller is used to guide and control the inverter to synthesize a stepped output voltage waveform with reduced harmonics. In this paper, three different intelligent soft-computing methods are used to design a fuzzy system to be used as a closed loop control system for regulating the inverter output. Gravitational Search Algorithm and Genetic Algorithm are used as optimization methods to evaluate switching angles for different combination of input voltages applied to MLI. Wavelet Transform is used as synthesizing technique to shape stepped output waveform of inverter using orthogonal wavelet sets. The proposed FLC controlled method is carried out for a wider range of input dc voltages by considering ±10% variations in nominal voltage value. A 7-level inverter is used to validate the results of proposed control methods. The three proposed methods are then compared in terms of various parameters like computational time, switching angles and THD to justify the performance and system flexibility. Finally, hardware based results are also obtained to verify the viability of the proposed method.</p>

Application Fuzzy Logic System for Accurate Sheet Trim Shower Position

2019 ◽  
Vol 3 (1) ◽  
pp. 186-192
Author(s):  
Yudi Wibawa
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Automation System ◽  
Paper Making ◽  
System A ◽  
Accurate Position ◽  
And Performance ◽  
Better Than ◽  
Logic System

This paper aims to study for accurate sheet trim shower position for paper making process. An accurate position is required in an automation system. A mathematical model of DC motor is used to obtain a transfer function between shaft position and applied voltage. PID controller with Ziegler-Nichols and Hang-tuning rule and Fuzzy logic controller for controlling position accuracy are required. The result reference explains it that the FLC is better than other methods and performance characteristics also improve the control of DC motor.

STABILISASI TEGANGAN KELUARAN BUCK CONVERTER DENGAN METODE FUZZY LOGIC CONTROLLER

JURNAL ELTEK ◽  
2018 ◽  
Vol 16 (2) ◽  
pp. 125
Author(s):  
Oktriza Melfazen
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Rise Time ◽  
Fuzzy Logic Controller ◽  
Buck Converter ◽  
Settling Time

Buck converter idealnya mempunyai keluaran yang stabil, pemanfaatandaya rendah, mudah untuk diatur, antarmuka yang mudah dengan pirantiyang lain, ketahanan yang lebih tinggi terhadap perubahan kondisi alam.Beberapa teknik dikembangkan untuk memenuhi parameter buckconverter. Solusi paling logis untuk digunakan pada sistem ini adalahmetode kontrol digital.Penelitian ini menelaah uji performansi terhadap stabilitas tegangankeluaran buck converter yang dikontrol dengan Logika Fuzzy metodeMamdani. Rangkaian sistem terdiri dari sumber tegangan DC variable,sensor tegangan dan Buck Converter dengan beban resistif sebagaimasukan, mikrokontroler ATMega 8535 sebagai subsistem kontroldengan metode logika fuzzy dan LCD sebagai penampil keluaran.Dengan fungsi keanggotaan error, delta error dan keanggotaan keluaranmasing-masing sebanyak 5 bagian serta metode defuzzifikasi center ofgrafity (COG), didapat hasil rerata error 0,29% pada variable masukan18V–20V dan setpoint keluaran 15V, rise time (tr) = 0,14s ; settling time(ts) = 3,4s ; maximum over shoot (%OS) = 2,6 dan error steady state(ess) = 0,3.

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