Optimization Control System using the Quantum Behaved Particle Swarm Optimization on Vehicle Steering Control System with Steer-by-Wire System

2014 ◽  
Vol 71 (2) ◽  
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Pid Control ◽  
Fuzzy Logic Control ◽  
Steering Control ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control ◽  
Steer By Wire ◽  
Wire System

Fuzzy Logic includes a technique are widely applied to the vehicle steering control system, however, to get the parameters required by a reliable Fuzzy Logic Control (FLC), needed training and learning process. Quantum behaved Particle Swarm Optimization (QPSO) is a simple optimization method that guarantees the achievement of global convergence quickly. This paper aimed to optimize of the steering control system on vehicle with steer-by-wire system using QPSO. The vehicle steering control system consists of Fuzzy Logic Control (FLC) and the Proportional, Integral and Derivative (PID) control are built in cascade, in which FLC is used to minimize the lateral motion error and PID control is used to suppress yaw motion error of the vehicle. The parameters of the control system are optimized by QPSO consists of three parameters to determine the position of the centre and the width of the triangle membership function of FLC and three constant gain of PID control. The optimization is done through the software in the loop simulation of vehicle models represented by 10 Degree of Freedom (DOF) of the vehicle dynamics. Simulation results showed that optimization using QPSO on the parameters of the control system can guarantee the movement of the vehicle is constantly maintained at the desired trajectory with a smaller error and higher vehicle speeds compared to the control system without tuned. The results obtained will be used as the basis for testing of the hardware in the loop simulation (HILS) so it can further improve the performance of steer-by-wire system. 

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Permanent Magnet Chatter Absorber with Fuzzy Logic Control

2009 ◽  
Vol 147-149 ◽  
pp. 290-295 ◽  
Author(s):  
Bogdan Broel-Plater ◽  
Stefan Domek ◽  
Arkadiusz Parus
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Logic Control ◽  
Control Algorithm ◽  
Permanent Magnets ◽  
Practical Experience ◽  
High Energy ◽  
Logic Control ◽  
Machine Operators

The paper deals with semi-active chatter absorber based on an electrodynamic transducer built around high-energy permanent magnets. Also, a fuzzy logic control system for the absorber control system has been designed. The principal advantage of fuzzy control is the possibility to implement practical experience gained by machine operators in the control algorithm. Hence, the possibility of factoring such quantities, as vibrations experienced by selected points of the machine-tool, and sound emitted by working machine into the analyzed chatter absorber fuzzy control system has been studied in the paper. The control system has been tested by way of simulation with the use of the process and cutting force models.

scholarly journals Reconfigurable fuzzy logic system for high-frame rate stereovision object tracking

2021 ◽  
Author(s):  
Oleg Samarin
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Object Tracking ◽  
Fuzzy Logic Control ◽  
Tracking System ◽  
Frame Rate ◽  
Disparity Map ◽  
High Frame Rate ◽  
Logic Control ◽  
Logic System

his study investigates the applicability of fuzzy logic control to high-frame rate stereovision object tracking. The technology developed in this work is based on utilizing a disparity map produced by the Stereovision Tracking System (STS) to identify the object of interest. The coordinates of the object are used by the fuzzy logic control system to provide rotation and focus control for object tracking. The fuzzy logic control was realized as a reconfigurable hardware module and implemented on Virtex-2 FPGA platform of the STS. The fuzzy reasoning was implemented as a reconfigurable look-up table residing in FPGA's internal memory. A set of software tools facilitating creation of loop-up table and reconfiguration of fuzzy logic control system was developed. Finally, the experimental prototype of the system was built and tested.

scholarly journals DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

Jurnal Teknik ◽  
2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Sumardi Sadi
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Logic Control ◽  
Control Command ◽  
Arduino Uno

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

Electromagnetic Levitator at Space Power Institute, Auburn University

2000 ◽  
Author(s):  
Bogdan O. Ciocirlan ◽  
Dan B. Marghitu ◽  
David G. Beale ◽  
Ruel A. Overfelt
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Analytical Model ◽  
Lorentz Force ◽  
Fuzzy Logic Control ◽  
Vertical Motion ◽  
Electromagnetic Levitation ◽  
Auburn University ◽  
Logic Control ◽  
Electromagnetic Levitator

Abstract In this paper, the electromagnetic levitation instrument designed by Space Power Institute at Auburn University is analyzed. An analytical model to compute the Lorentz force that supports the specimen against gravity is proposed. The equation of vertical motion of the specimen is developed and linearized. A fuzzy logic control system designed to stabilize the motion of levitated specimens is also presented.

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