Article Search
닫기

Original Article

Split Viewer

International Journal of Fuzzy Logic and Intelligent Systems 2022; 22(1): 69-77

Published online March 25, 2022

https://doi.org/10.5391/IJFIS.2022.22.1.69

© The Korean Institute of Intelligent Systems

Trajectory-Tracking Control of a Transport Robot for Smart Logistics Using the Fuzzy Controller

Young-Jae Ryoo

Department of Electrical and Control Engineering, Mokpo National University, Muan, Jeonnam, Korea

Correspondence to :
Young-Jae Ryoo (yjryoo@mokpo.ac.kr)

Received: January 11, 2022; Revised: March 7, 2022; Accepted: March 10, 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

This paper presents the trajectory-tracking control of transport robots for smart logistics using a fuzzy controller. We propose a new method of designing tracking control using the speed regulator based on an interval type-2 fuzzy logic system for automated transport robots, to track the complex predefined trajectory paths. The proposed controller with a speed regulator can help the robot to slow down on curved paths and increase its speed on straight tracks. In this study, the simulation results show that the proposed method has better performance and higher reliability than the type-1 fuzzy control.

Keywords: Trajectory tracking control, Transport robot, Interval type-2 fuzzy logic system, Speed regulator

This research was supported by the Research Fund of the Mokpo National University in 2019.

No potential conflict of interest relevant to this article was reported.

Young-Jae Ryoo received his Ph.D., M.S, and B.S. degrees in the Department of Electrical Engineering, Chonnam National University, Korea in 1998, 1993, and 1991, respectively. He was a visiting researcher in North Carolina A&T State University, USA in 1999. He was a visiting professor in the Department of Mechanical Engineering, Virginia Tech, USA from 2010 to 2012. He is currently a professor in the Department of Electrical and Control Engineering, Mokpo National University, South Korea from 2000. He also serves as a director with the intelligent space laboratory in Mokpo National University, where he is responsible for research projects in the area of intelligence, robotics, and vehicles. He served as the president of the Korean Institute of Intelligent Systems in 2021. He is currently a board member of KIIS, an editor for the Journal of Korean Institute of Electrical Engineering from 2010, an editor for the Journal of Fuzzy Logic and Intelligent Systems from 2009, and a committee member of the International Symposium on Advanced Intelligent Systems from 2005. He served as a general chair of the International Symposium on Advanced Intelligent System in 2014 and 2015. He won the outstanding paper awards, the best presentation awards, and the recognition awards in International Symposiums on Advanced Intelligent Systems. He is the author of over 200 technical publications. His research interests include intelligent space, humanoid robotics, legged robotics, autonomous vehicles, unmanned vehicles, wheeled robotics, and biomimetic robotics.

E-mail: yjryoo@mokpo.ac.kr


Article

Original Article

International Journal of Fuzzy Logic and Intelligent Systems 2022; 22(1): 69-77

Published online March 25, 2022 https://doi.org/10.5391/IJFIS.2022.22.1.69

Copyright © The Korean Institute of Intelligent Systems.

Trajectory-Tracking Control of a Transport Robot for Smart Logistics Using the Fuzzy Controller

Young-Jae Ryoo

Department of Electrical and Control Engineering, Mokpo National University, Muan, Jeonnam, Korea

Correspondence to:Young-Jae Ryoo (yjryoo@mokpo.ac.kr)

Received: January 11, 2022; Revised: March 7, 2022; Accepted: March 10, 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents the trajectory-tracking control of transport robots for smart logistics using a fuzzy controller. We propose a new method of designing tracking control using the speed regulator based on an interval type-2 fuzzy logic system for automated transport robots, to track the complex predefined trajectory paths. The proposed controller with a speed regulator can help the robot to slow down on curved paths and increase its speed on straight tracks. In this study, the simulation results show that the proposed method has better performance and higher reliability than the type-1 fuzzy control.

Keywords: Trajectory tracking control, Transport robot, Interval type-2 fuzzy logic system, Speed regulator

Fig 1.

Figure 1.

Model of a transport robot.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 69-77https://doi.org/10.5391/IJFIS.2022.22.1.69

Fig 2.

Figure 2.

Velocity control of the mobile robot.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 69-77https://doi.org/10.5391/IJFIS.2022.22.1.69

Fig 3.

Figure 3.

Trajectory-tracking control with speed regulator.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 69-77https://doi.org/10.5391/IJFIS.2022.22.1.69

Fig 4.

Figure 4.

Block diagram of the fuzzy-based speed regulator.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 69-77https://doi.org/10.5391/IJFIS.2022.22.1.69

Fig 5.

Figure 5.

Block diagram of the fuzzy logic controller.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 69-77https://doi.org/10.5391/IJFIS.2022.22.1.69

Fig 6.

Figure 6.

Membership of the position error eɛ.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 69-77https://doi.org/10.5391/IJFIS.2022.22.1.69

Fig 7.

Figure 7.

Membership of the change in error deɛ.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 69-77https://doi.org/10.5391/IJFIS.2022.22.1.69

Fig 8.

Figure 8.

Type-2 fuzzy logic system model.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 69-77https://doi.org/10.5391/IJFIS.2022.22.1.69

Fig 9.

Figure 9.

MATLAB/Simulink block diagram for the simulation test.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 69-77https://doi.org/10.5391/IJFIS.2022.22.1.69

Fig 10.

Figure 10.

The testing trajectory path.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 69-77https://doi.org/10.5391/IJFIS.2022.22.1.69

Fig 11.

Figure 11.

Robot’s position error according to control methods.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 69-77https://doi.org/10.5391/IJFIS.2022.22.1.69

Fig 12.

Figure 12.

Robot’s speed according to control methods.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 69-77https://doi.org/10.5391/IJFIS.2022.22.1.69

Table 1 . Fuzzy rule table.

Γdeɛ
VNDENDEZDEPDEVPDE
eɛVNEN2N2N2N1Z
NEN2N1N1ZP1
ZEN2N1ZP1P2
PEN1ZP1P1P2
VPEZP1P2P2P2

Table 2 . Performance comparison according to various speed regulators.

Type of speed regulatorAVMPE (cm)RMSE (cm)MPPER (cm)Completion time (s)
Conv.5.71.55663.4106.76
T1FLS5.21.54263.197.97
T2FLS (KM)5.21.47733.1102.11
T1FLS, ϑL = 04.91.33063.3117.81
T2FLS (KM), ϑL = 04.71.29683.3119.79

Conv., conventional (without FLS)..


Share this article on :

Most KeyWord