Intelligent PID Controller for Smart Building Comfort Temperature Control Basedon Fuzzy Logic and Neural Networks

2017 ◽  
Vol 27 (6) ◽  
pp. 522-529
Author(s):  
Young Im Cho ◽  
Aigerim Altayeva
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Temperature Control ◽  
Pid Controller ◽  
Smart Building ◽  
Intelligent Pid

Enhanced Fuzzy Logic Controller for Simulated HE Temperature Control System

2015 ◽  
Vol 1113 ◽  
pp. 715-722
Author(s):  
Mohd Aizad Ahmad ◽  
Abdul Aziz Ishak ◽  
Kamariah Noor Ismail
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Heat Exchanger ◽  
Temperature Control ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Energy Balance Equation ◽  
Temperature Control System ◽  
Rule Bases

This paper presents the performances of an enhanced fuzzy logic controller (EFLC) for simulated Heat Exchanger (HE) temperature control system. The HE system is modeled mathematically using Energy Balance Equation and simulated using MATLAB/Simulink software. The Fuzzy Inference Structure (FIS) used was Sugeno-type. EFLC comprises of two parts which are normalized FLC part and model reference (MR) part. Both normalized and MRFLC part was using Gaussian membership function (MF) with 7x7 rule bases. Set Point (SP) tests conducted for change from 43°C to 39°C, 39°C to 35°C and 43°C to 35°C. The performances on SP tests of the FLC and proposed EFLC were compared to PID controller. The results showed that EFLC produced lower decay ratio (DR) with less oscillations, reduced undershoot (US), shorter settling time (Ts) and minimum Integral Absoluter Error (IAE) compare to FLC and PID controller.

scholarly journals Feedback-feedforward fuzzy logic approach for temperature control in bioethanol vacuum distiller

2019 ◽  
Vol 16 (2) ◽  
pp. 678
Author(s):  
Muhammad Aziz Muslim ◽  
Tegar Sukma Yudha ◽  
B.S.K.K. Ibrahim
Keyword(s):  
Fuzzy Logic ◽  
Temperature Control ◽  
Pid Controller ◽  
Fossil Fuels ◽  
Fuzzy Logic Controller ◽  
Major Research ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Sugar Extraction ◽  
Temperature And Pressure

<span>Energy conservation and diversification are becoming a major research issue. Awareness of the limited sources of energy from fossil fuels encourages research on renewable energy. Bioethanol is a promising fuel substitute for gasoline. Bioethanol processing includes sugar extraction, fermentation, distillation, and absorption. Temperature and pressure controls are essential in bioethanol processing. This paper presents a feedback-feedforward fuzzy logic approach for temperature control in a bioethanol vacuum distiller. In this study, vacuum pressure is employed as feedforward inputs for a fuzzy logic controller. The feedforward input directly modifies the main controller, i.e., fuzzy logic controller, through fuzzy rules. The controller is implemented using Arduino Mega 2560 microcontroller. The results show that the proposed feedback-feedforward fuzzy logic controller could successfully maintain the temperature at the desired setpoint value with small steady-state error (3.85%) and relatively shorter settling time compared to classical PID controller and fuzzy logic controller.</span>

Comparative Study of DC Motor Speed Control Using Neural Networks and Fuzzy Logic Controller

2015 ◽  
Author(s):  
Wafa Batayneh ◽  
Nash’at Nawafleh
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Speed Control ◽  
Dc Motor ◽  
Motor Speed ◽  
The Neural Network ◽  
Intelligent Controllers ◽  
Rising Time

This paper demonstrates the importance of the intelligent controllers over the conventional methods. A speed control of the DC motor is developed using both Neural Networks and Fuzzy logic controller in MATLAB environment as intelligent controllers. In addition a conventional PID controller is developed for comparison purposes. Both intelligent controllers are designed based on the simulation results of the nonlinear equations in addition to the expert pre knowledge of the system. The output response of the system is obtained using the two types of the intelligent controllers, in addition to the conventional PID controller. The performance of the designed Neural Networks, Fuzzy logic controller and the PID controller is compared and investigated. Finally, the results show that the neural network has minimum overshoot, and minimum steady state parameters. This shows more efficiency of the intelligent controllers over the conventional PID controller. Also it shows that Neural Networks is better than Fuzzy logic controller in terms of over shoot and rising time. At the end of this paper an implementation of Graphical User Interface (GUI) method is developed. The main purpose of the GUI is to give the users a chance to use the program in a simple way without the need to understand the program languages.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

Withdrawn: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC)

2019 ◽  
Vol 13 ◽  
Author(s):  
Abeer A. Amer ◽  
Soha M. Ismail
Keyword(s):  
Diabetes Mellitus ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Computer Science ◽  
Recent Advances ◽  
Vascular Center ◽  
Copyright Permission ◽  
Legal Right

The following article has been withdrawn on the request of the author of the journal Recent Advances in Computer Science and Communications (Recent Patents on Computer Science): Title: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC) Authors: Abeer A. Amer and Soha M. Ismail* Bentham Science apologizes to the readers of the journal for any inconvenience this may cause BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

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