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.

A migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller

2016 ◽  
Vol 32 ◽  
Author(s):  
Ping-Huan Kuo ◽  
Tzuu-Hseng S. Li ◽  
Guan-Yu Chen ◽  
Ya-Fang Ho ◽  
Chih-Jui Lin
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Fuzzy Logic Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Collective Behaviors ◽  
Navigation Method

AbstractObstacle avoidance is an important issue in robotics. In this paper, the particle swarm optimization (PSO) algorithm, which is inspired by the collective behaviors of birds, has been designed for solving the obstacle avoidance problem. Some animals that travel to the different places at a specific time of the year are called migrants. The migrants also represent the particles of PSO for defining the walking paths in this work. Migrants consider not only the collective behaviors, but also geomagnetic fields during their migration in nature. Therefore, in order to improve the performance and the convergence speed of the PSO algorithm, concepts from the migrant navigation method have been adopted for use in the proposed hybrid particle swarm optimization (H-PSO) algorithm. Moreover, the potential field navigation method and the designed fuzzy logic controller have been combined in H-PSO, which provided a good performance in the simulation and the experimental results. Finally, the Federation of International Robot-soccer Association (FIRA) HuroCup Obstacle Run Event has been chosen for validating the feasibility and the practicability of the proposed method in real time. The designed adult-sized humanoid robot also performed well in the 2015 FIRA HuroCup Obstacle Run Event through utilizing the proposed H-PSO.

scholarly journals A New Opinion Mining Method based on Fuzzy Classifier and Particle Swarm Optimization (PSO) Algorithm

2018 ◽  
Vol 18 (2) ◽  
pp. 36-50
Author(s):  
Samira Bordbar ◽  
Pirooz Shamsinejad
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Opinion Mining ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Fuzzy Rules ◽  
Training Data ◽  
Fuzzy Classifier ◽  
Swarm Optimization ◽  
Main Challenge

Abstract Opinion Mining or Sentiment Analysis is the task of extracting people final opinion about something through their unstructured sentiments. The Opinion Mining process is as follows: first, product features which are most important to a user are extracted from his/her comments. Then, sentiments will be emotionally classified using their emotional implications. In this paper we propose an opinion classification method based on Fuzzy Logic. Up to now, a few methods have taken advantage of fuzzy logic in opinion classification and all of them have imported fuzzy rules into system as background knowledge. But the main challenge here is finding the fuzzy rules. Our contribution is to automatically extract fuzzy rules and their parameters from training data. Here we have used the Particle Swarm Optimization (PSO) algorithm to extract fuzzy rules from training data. Also, for better results we have devised a mutation-based PSO. All proposed methods have been implemented and tested on relevant data. Results confirm that our method can reach better accuracy than current state of the art methods in this domain.

scholarly journals Solar System Array by Fuzzy Logic based on MPPT Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 1132-1139
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Solar System ◽  
Pid Controller ◽  
Performance Enhancement ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point

In recent times a huge attention has been given on development of proper planning In this paper we present a top dimension perspective on forefront status of Closed circle ID system the use of PID Controller from explicit creators. The proportional– integral– subsidiary (PID) controller is the most extreme comprehensively ordinary controller inside the business bundles, specifically in strategy enterprises in light of fabulous expense to profit proportion. In this paper we focus on MPPT based solar system performance enhancement by use of fuzzy logic controller’s designs optimized by particle swarm optimization (PSO). We have described about different latest A.I. techniques that has been hybrid with fuzzy logic for improving PV array based solar plants performance in recent time. The artificial intelligence technique applied in this work is the Particle Swarm Optimization (PSO) algorithm and is used to optimize the membership functions for maximum power point tracking rule set of the FLC. By using PSO algorithm, the optimized FLC is able to maximize energy to the system loads while also maintaining a higher stability and speed as compared to P& O based MPPT algorithm

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 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.

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