Article
Optimization of Fuzzy Logic Controller Used for a Differential
Drive Wheeled Mobile Robot
Alexandr Štefek
1
, Van Thuan Pham
1,
* , Vaclav Krivanek
2
and Khac Lam Pham
3
Citation: Štefek, A.; Pham, V.T.;
Krivanek, V.; Pham, K.L.
Optimization of Fuzzy Logic
Controller Used for a Differential
Drive Wheeled Mobile Robot. Appl.
Sci. 2021, 11, 6023. https://doi.org/
10.3390/app11136023
Academic Editor: Dario Richiedei
Received: 31 May 2021
Accepted: 25 June 2021
Published: 29 June 2021
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4.0/).
1
Department of Informatics and Cyber Operations, University of Defence, Kounicova 65,
662 10 Brno, Czech Republic; alexandr.stefek@unob.cz
2
Department of Military Robotics, University of Defence, Kounicova 65, 662 10 Brno, Czech Republic;
vaclav.krivanek@unob.cz
3
Department of Aviation Technology, University of Defence, Kounicova 65, 662 10 Brno, Czech Republic;
khaclam.pham@unob.cz
* Correspondence: vanthuan.pham@unob.cz or vanthuanhvhq@gmail.com
Abstract:
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.
Keywords:
differential drive robots; fuzzy logic control; genetic algorithms; optimal control; wheeled
mobile robot control; pymoo
1. Introduction
The Differential Drive Wheeled Mobile Robot (DDWMR) can perform many different
tasks in many fields. To fulfill its series of tasks in different places, the robot has to move
from one place to another place in a known or an unknown environment. Therefore, motion
control problems such as path tracking, “go-to-goal”, “point-to-point”, waypoints tracking,
pose tracking, etc. of a mobile robot are still the crucial and fundamental problems for
the robot operation. These control problems have been widely researched and published,
such as the obstacle avoidance with minimum travel time [
1
], the go-to-goal control
without obstacle avoidance [
2
,
3
], the leader following control [
4
], the trajectory tracking
control [
5
–
7
], the wall-following control [
8
], the obstacle avoidance [
9
–
11
]. For the robot
motion control, both conventional control methods and modern control methods have been
applied. Several well-known controllers such as the controller proposed by Kanayama and
Robins Mathew, the feedback-based controller for the circular path, the Lyapunov-based
controller, the clever trigonometry-based controller, and the Dubins path-based controller,
have been discussed in [12].
Appl. Sci. 2021, 11, 6023. https://doi.org/10.3390/app11136023 https://www.mdpi.com/journal/applsci
