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

scholarly journals The Optimization of Marine Diesel Engine Rotational Speed Control Process by Fuzzy Logic Control Based on Particle Swarm Optimization Algorithm

2018 ◽  
Vol 10 (10) ◽  
pp. 99 ◽  
Author(s):  
Tien Tran
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Diesel Engine ◽  
Fuzzy Logic Control ◽  
Engine Speed ◽  
Fuzzy Logic Controller ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Logic Control

The marine main diesel engine rotational speed automatic control plays a significant role in determining the optimal main diesel engine speed under impacting on navigation environment conditions. In this article, the application of fuzzy logic control theory for main diesel engine speed control has been associated with Particle Swarm Optimization (PSO). Firstly, the controller is designed according to fuzzy logic control theory. Secondly, the fuzzy logic controller will be optimized by Particle Swarm Optimization (PSO) in order to obtain the optimal adjustment of the membership functions only. Finally, the fuzzy logic controller has been completely innovated by Particle Swarm Optimization algorithm. The study results will be represented under digital simulation form, as well as comparison between traditional fuzzy logic controller with fuzzy logic control–particle swarm optimization speed controller being obtained.

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. 

Modeling and Control of Wheeled Mobile Robot With Four Mecanum Wheels

2021 ◽  
Vol 39 (5A) ◽  
pp. 779-789
Author(s):  
Sameh F. Hasana ◽  
Hassan. M. Alwan
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Mobile Robot ◽  
Dynamic Models ◽  
Particle Swarm ◽  
Wheeled Mobile Robot ◽  
Swarm Optimization ◽  
Modeling And Control

This work presents a driving control for the trajectory tracking of four mecanum wheeled mobile robot (FMWMR). The control consists of Backstepping-Type 1 Fuzzy Logic-Particle swarm optimization i.e.,(BSC-T1FLC-PSO). The kinematic and dynamic models have been derived. Backstepping controller (BSC) is used for finding controlled torques that generated from robot motors while Type-1 fuzzy logic control (T1FLC) as well as particle swarm optimization (PSO) used for finding the appropriate values of gain parameters of BSC. Square trajectory has been selected to test the performance of the control system of FMWMR. MATLAB/ Simulink is used to simulate the results. It has been concluded from the results that obtained from this control system there is a good matching between the simulated and the desired trajectories.

scholarly journals Effect of Fuzzy Logic Based-Skyhook Policy with Particle Swarm Optimization for Semi-Active Ride Comfort

2018 ◽  
Vol 7 (4.36) ◽  
pp. 409 ◽  
Author(s):  
Mat Hussin Ab Talib ◽  
Intan Zaurah Mat Darus ◽  
Pakharuddin Mohd Samin
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Ride Comfort ◽  
Particle Swarm ◽  
Magnetorheological Damper ◽  
Swarm Optimization ◽  
Logic Control ◽  
Force Velocity ◽  
Spencer Model ◽  
Force Displacement

This paper presents the effect of the fuzzy logic based-skyhook policy tuned using particle swarm optimization (FLSP-PSO) for semi-active ride comfort of quarter vehicle model. Spencer model was used to represent the magnetorheological damper model and its behavior was investigated in the form of force-displacement and force-velocity characteristics. The fuzzy logic control adopted with the skyhook policy based on Sugeno-type fuzzy was used to enhance the ride performance. An intelligent evolutionary algorithm known as the particle swarm optimization was also adapted in the proposed controller to compute the fuzzy gain scaling. The performance of the FLSP-PSO controller is compared to other controller responses. The effect of the PSO techniques to optimize the FLPS parameters gives a better performance and able to improve the vehicle ride comfort than its counterparts.  

scholarly journals Anti sway tuned control of gantry cranes

2021 ◽  
Vol 3 (8) ◽  
Author(s):  
Vladimir A. Suvorov ◽  
Mohammad Reza Bahrami ◽  
Evgeniy E. Akchurin ◽  
Ivan A. Chukalkin ◽  
Stanislav A. Ermakov ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
High Speed ◽  
Low Cost ◽  
Particle Swarm ◽  
Optimization Method ◽  
Angular Speed ◽  
Overhead Crane ◽  
Swarm Optimization ◽  
Speed Regulator

Abstract Load swaying is one of the most frequently occurring problems at production sites. The purpose of this work is to create a control system for the movement of an overhead crane with an anti-sway function. The Particle Swarm Optimization method has been used to find the controller coefficients. The crane movement with the anti-sway function should be implemented using a PLC (programmable logic controller) and have a high speed of operation. The frequency converter controls the speed of the drive that moves the crane. The main advantage of the system is its simplicity and low cost combined with the low swaying of the load. The oscillation amplitude with an angular speed regulator is two to three times less in comparison with the control system without the angular speed regulator. The presence of an angular speed regulator minimizes the impact of the load weight and the rope length. The efficiency of the simulator program for calculating angular speed has been tested and confirmed. Verification of the created mathematical model of the crane with experimental installation has been made. Article Highlights An efficient and low-cost anti-sway system for overhead cranes has been developed. The efficiency of the system was tested experimentally, the dependencies of the influence of factors on the sway angle were obtained. The selection of the regulator coefficients is implemented using the particle swarm optimization method coded in C++, which provides high-speed performance and the ability to integrate the algorithm into the PLC of the overhead crane control system.

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