Differential Drive Wheeled Mobile Robot (WMR) Control Using Fuzzy Logic Techniques

2010 ◽  
Author(s):  
Razif Rashid ◽  
Irraivan Elamvazuthi ◽  
Mumtaj Begam ◽  
M. Arrofiq
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Wheeled Mobile Robot ◽  
Differential Drive

scholarly journals Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot

2021 ◽  
Vol 11 (13) ◽  
pp. 6023
Author(s):  
Alexandr Štefek ◽  
Van Thuan Pham ◽  
Vaclav Krivanek ◽  
Khac Lam Pham
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Motion Control ◽  
Fuzzy Logic Controller ◽  
Operation Time ◽  
Wheeled Mobile Robot ◽  
Optimization Process ◽  
Differential Drive ◽  
Full Optimization

The energy-efficient motion control of a mobile robot fueled by batteries is an especially important and difficult problem, which needs to be continually addressed in order to prolong the robot’s independent operation time. Thus, in this article, a full optimization process for a fuzzy logic controller (FLC) is proposed. The optimization process employs a genetic algorithm (GA) to minimize the energy consumption of a differential drive wheeled mobile robot (DDWMR) and still ensure its other performances of the motion control. The earlier approaches mainly focused on energy reduction by planning the shortest path whereas this approach aims to optimize the controller for minimizing acceleration of the robot during point-to-point movement and thus minimize the energy consumption. The proposed optimized controller is based on fuzzy logic systems. At first, an FLC has been designed based on the experiment and as well as an experience to navigate the DDWMR to a known destination by following the given path. Next, a full optimization process by using the GA is operated to automatically generate the best parameters of all membership functions for the FLC. To evaluate its effectiveness, a set of other well-known controllers have been implemented in Google Colab® and Jupyter platforms in Python language to compare them with each other. The simulation results have shown that about 110% reduction of the energy consumption was achieved using the proposed method compared to the best of six alternative controllers. Also, this simulation program has been published as an open-source code for all readers who want to continue in the research.

scholarly journals Robust ℋ∞-Fuzzy Logic Control for Enhanced Tracking Performance of a Wheeled Mobile Robot in the Presence of Uncertain Nonlinear Perturbations

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3673 ◽  
Author(s):  
Nur Ahmad
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Wheeled Mobile Robot ◽  
Tracking Performance ◽  
Model Parameters ◽  
Control Law ◽  
System Modelling ◽  
Nonlinear Perturbations ◽  
Model Errors ◽  
Dc Motors

Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling will be a considerable aid in synthesizing the compensator. The focus of this paper is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to model parametric uncertainties and uncertain deadzones. For the system at hand, the uncertain nonlinear perturbations are greatly induced by the time-varying power supply, followed by behaviour of motion and speed. In this work, the system is firstly modelled, where correlations between the model parameters and different input datasets as well as voltage supply are obtained via polynomial regressions. A robust H ∞ -fuzzy logic approach is then proposed to treat the issues due to the aforementioned perturbations. Via the proposed strategy, the H ∞ controller and the fuzzy logic (FL) compensator work in tandem to ensure the control law is robust against the model uncertainties. The proposed technique was validated via several real-time experiments, which showed that the speed and path tracking performance can be considerably enhanced when compared with the results via the H ∞ controller alone, and the H ∞ with the FL compensator, but without the presence of the robust control law.

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.

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