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.

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

Particle Swarm Optimization of a Fuzzy Logic Controller for a Mobile Robot Tracking a Moving Target

2013 ◽  
Vol 373-375 ◽  
pp. 1237-1241 ◽  
Author(s):  
Karim Benbouabdallah ◽  
Qi Dan Zhu
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Mobile Robot ◽  
Target Tracking ◽  
Fuzzy Logic Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Moving Target ◽  
Swarm Optimization ◽  
Control Approach

Target tracking is one of the interesting tasks of mobile robots navigation. This paper presents a fuzzy logic controller (FLC) for a mobile robot chasing a moving target. The proposed control strategy computes both the linear and angular velocities of the robot in order to regulate its perceptual state according to the targets motion. Then, a particle swarm optimization (PSO) algorithm is applied to tune the scaling factors of the FLC for a better performance in term of target tracking. Simulation results demonstrate the effectiveness of the proposed control approach.

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

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