scholarly journals Comparison between proportional, integral, derivative controller and fuzzy logic approaches on controlling quarter car suspension system

2018 ◽  
Vol 184 ◽  
pp. 02018
Author(s):  
Ahmet Mehmet Karadeniz ◽  
Alsabbagh Ammar ◽  
Husi Dr.Geza
Keyword(s):  
Fuzzy Logic ◽  
Public Transportation ◽  
Fuzzy Logic Controller ◽  
Suspension System ◽  
Maximum Efficiency ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Car Suspension ◽  
Quarter Car

Developing and constantly changing technologies, efforts to achieve maximum efficiency with minimum fuel consumption, as well as the development of comfort and safety systems, have become very essential topic in car manufacturing and design. Whereas comfort and security were not given a high importance in the first produced cars, they are indispensable elements of today's automobiles. Since public transportation uses road in large scale, the need for safety and repose is also increasing. Nowadays, vehicles have better security and comfort systems, which react very quickly to all kinds of loads and different cases of driving (braking, acceleration, high speed, cornering), where the tires can keep the road at its best, utilizing an advanced suspension system. In this study, a quarter-car model was fulfilled using LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench) software. The control of this model has been realized by applying two different controllers. PID (proportional, integral, derivative) controller which is a common and conventional control method and the Fuzzy Logic controller which is considered as an expert system that is becoming more and more widely used. In both control approaches, controlling the suspension system was achieved successfully. However; It has been determined that controlling the system using Fuzzy Logic controller gave better dynamic response than applying the PID controller for the quarter car suspension model that has been used in the direction of this study.

A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

2019 ◽  
Vol 26 (13-14) ◽  
pp. 1187-1198 ◽  
Author(s):  
Li-Xin Guo ◽  
Dinh-Nam Dao
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Deep Learning ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

scholarly journals Design and simulation of a normalized fuzzy logic controller for the quadruple-tank process

2020 ◽  
Vol 18 (1) ◽  
pp. 227
Author(s):  
Wameedh Riyadh Abdul-Adheem
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Industrial Processes ◽  
Stable Operation ◽  
Design Tools ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Control Techniques ◽  
Classical Control

<p class="MsoNormal" style="margin-top: 6.0pt; text-align: justify;">Industrial processes include multivariable systems and nonlinearities. These conditions must be effectively controlled to ensure a stable operation. A proportional–integral–derivative controller and other classical control techniques provide simple design tools to designers, but cannot accommodate nonlinearities in industrial processes. In this study, the quadruple-tank process, which is one of the most widely used processes in the chemical industry, was selected as the research object.  To examine this process, a fuzzy logic controller, instead of an exact mathematical model, was proposed to ensure the reliability of the experience. A modification was proposed to facilitate the design process. To check the validity of the proposed controller, it was compared with the conventional proportional–integral controller. The former exhibited acceptable performance.</p><table class="MsoTableGrid" style="width: 444.85pt; border-collapse: collapse; border: none; mso-border-alt: solid windowtext .5pt; mso-yfti-tbllook: 1184; mso-padding-alt: 0cm 5.4pt 0cm 5.4pt;" width="0" border="1" cellspacing="0" cellpadding="0"><tbody><tr style="mso-yfti-irow: 0; mso-yfti-firstrow: yes; mso-yfti-lastrow: yes; height: 63.4pt;"><td style="width: 290.6pt; border: none; border-top: solid windowtext 1.0pt; mso-border-top-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; height: 63.4pt;" valign="top" width="593"><p class="MsoNormal" style="margin-top: 6.0pt; text-align: justify;"><span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi;">Industrial processes include multivariable systems and nonlinearities. These conditions <span style="mso-no-proof: yes;">must be effectively</span> controlled to ensure a stable operation. A proportional–integral–derivative controller and other classical control techniques provide <span style="mso-no-proof: yes;">simple</span> design tools to designers, <span style="mso-no-proof: yes;">but</span> cannot accommodate nonlinearities in industrial processes. In this study, the quadruple</span><span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi; mso-bidi-language: AR-IQ;">-tank process</span><span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi;">, which is one of the most widely used processes in the chemical industry, was selected as the research object.<span style="mso-spacerun: yes;">  </span>To examine this process, a</span><span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi; mso-bidi-language: AR-IQ;"> fuzzy logic controller, instead of an </span><span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi;">exact mathematical model,</span><span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi;">was<span style="mso-no-proof: yes;"> proposed</span> to ensure the reliability of the experience. </span><span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi; mso-bidi-language: AR-IQ;">A modification was proposed to facilitate the design process. To check the <span style="mso-no-proof: yes;">validity</span> of the proposed controller, it was compared with the conventional proportional</span><span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi;">–</span><span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi; mso-bidi-language: AR-IQ;">integral <span style="mso-no-proof: yes;">controller. The former exhibited</span> <span style="mso-no-proof: yes;">acceptable</span> performance.</span></p><p class="MsoNormal" style="margin-top: 6.0pt; text-align: justify;"> </p></td></tr></tbody></table>

scholarly journals Design of microcontroller-based decentralized controller board to drive chiller systems using PID and fuzzy logic algorithms

2019 ◽  
Vol 234 (1) ◽  
pp. 98-107 ◽  
Author(s):  
Yalcin Isler ◽  
Savas Sahin ◽  
Orhan Ekren ◽  
Cuneyt Guzelis
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Low Cost ◽  
Variable Speed ◽  
Proportional Integral Derivative ◽  
Scroll Compressor ◽  
Proportional Integral ◽  
Output Controller ◽  
Expansion Valve ◽  
Controller Board

This study deals with designing a decentralized multi-input multi-output controller board based on a low-cost microcontroller, which drives both parts of variable-speed scroll compressor and electronic-type expansion valve simultaneously in a chiller system. This study aims to show the applicability of commercial low-cost microcontroller to increase the efficiency of the chiller system, having variable-speed scroll compressor and electronic-type expansion valve with a new electronic card. Moreover, the refrigerant system proposed in this study provides the compactness, mobility, and flexibility, and also a decrease in the controller unit’s budget. The study was tested on a chiller system that consists of an air-cooled condenser, a variable-speed scroll compressor, and a stepper driven electronic-type expansion valve. The R134a was used as a refrigerant fluid and its flow was controlled by electronic-type expansion valve in this setup. Both variable-speed scroll compressor and electronic-type expansion valve were driven by the proposed hardware using either proportional integral derivative or fuzzy logic controller, which defines four distinct controller modes. The experimental results show that fuzzy logic controlled electronic-type expansion valve and proportional integral derivative controlled variable-speed scroll compressor mode give more robustness by considering the response time.

Comparative Study of Fuzzy PID and PID controller optimized with Spider Monkey Optimization for a Robotic Manipulator System

2019 ◽  
Vol 12 ◽  
Author(s):  
Alka Agrawal ◽  
Vishal Goyal ◽  
Puneet Mishra
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Optimization Technique ◽  
Absolute Error ◽  
Robotic Manipulator ◽  
Spider Monkey ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Spider Monkey Optimization

Background: Robotic manipulator system has been useful in many areas like chemical industries, automobile, medical fields etc. Therefore, it is essential to implement a controller for controlling the end position of a robotic armeffectively. However, with the increasing non-linearity and the complexities of a robotic manipulator system, a conventional Proportional-Integral-Derivative controller has become ineffective. Nowadays, intelligent techniques like fuzzy logic, neural network and optimization algorithms has emerged as an efficient tool for controlling the highly complex non-linear functions with uncertain dynamics. Objective: To implement an efficient and robustcontroller using Fuzzy Logic to effectively control the end position of Single link Robotic Manipulator to follow the desired trajectory. Methods: In this paper, a Fuzzy Proportional-Integral-Derivativecontroller is implemented whose parameters are obtainedwith the Spider Monkey Optimization technique taking Integral of Absolute Error as an objective function. Results: Simulated results ofoutput of the plants controlled byFuzzy Proportional-Integral-Derivative controller have been shown in this paper and the superiority of the implemented controller has also been described by comparing itwith the conventional Proportional-Integral-Derivative controller and Genetic Algorithm optimization technique. Conclusion: From results, it is clear that the FuzzyProportional-Integral-Derivativeoptimized with the Spider monkey optimization technique is more accurate, fast and robust as compared to the Proportional-Integral-Derivativecontroller as well as the controllers optimized with the Genetic algorithm techniques.Also, by comparing the integral absolute error values of all the controllers, it has been found that the controller optimized with the Spider Monkey Optimization technique shows 99% better efficacy than the genetic algorithm technique.

Parametric Study on Proportional Integral Derivative Controlled Semi-Active Suspension System

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
E.M Allam ◽  
M.A.A Emam ◽  
Eid.S Mohamed
Keyword(s):  
Active Suspension ◽  
Suspension System ◽  
Proportional Integral Derivative ◽  
Active System ◽  
Proportional Integral ◽  
Quarter Car Model ◽  
Suspension Systems ◽  
Working Space ◽  
Active Suspension Systems ◽  
Quarter Car

This paper presents the effect of the suspension working space, body displacement, body acceleration and wheel displacement for the non-controlled suspension system (passive system) and the controlled suspension system of a quarter car model (semi-active system), and comparison between them. The quarter car passive and semi-active suspension systems are modelled using Simulink. Proportional Integral Derivative controllers are incorporated in the design scheme of semi-active models. In the experimental work, the influence of switchable damper in a suspension system is compared with the passive and semi-active suspension systems.

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