Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-24T16:21:02.719Z Has data issue: false hasContentIssue false

Stability Improvement of Segway Based on Tire Model Using the SEA

Published online by Cambridge University Press:  28 February 2020

Haneul Yun
Affiliation:
Pusan National University
Hongyu Zhang
Affiliation:
Pusan National University
Jangmyung Lee*
Affiliation:
Pusan National University
*
*Corresponding author. E-mail: [email protected]

Summary

This study proposes the use of a series elastic actuator (SEA) in a Segway to improve the stability of the tires during linear and curved driving, thus improving the comfort of the driver and ensuring driving stability. Recently, Segway has been developed continuously for intelligent mobile vehicles and the performance of Segway is being enhanced. Therefore, safety factors during the Segway driving have been considered seriously. In most of the developments and studies on Segway, the optimization and improvement of the controller component have been tackled and there are few studies on the safety devices and the stability of driving. The impact and vibration generated from the ground due to uneven road surfaces considerably influence the force exerted on the tire, which further affects driving stability. This research focuses on the control of the SEA based on the tire model to improve the driving stability of Segway. The performance of the proposed algorithm to improve the stability of the driver has been verified by straight and curved paths driving experiments with the tire model.

Type
Articles
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Joh, J. W. and Park, G. T., “Reasonable Hardware Design Methods for 2-Wheeled Mobile Robots: Based on Segway Type Mobile Robots,” Proceedings of ICS2009-KIEE (2009) pp. 109111.Google Scholar
Bageant, M. R., “Balancing a Two-Wheeled Segway Robot,” Bachelor’s Thesis (Massachusetts Institute of Technology, 2011).Google Scholar
Kwak, S. F., Lee, M. H. and Choi, B. J., “Design of fuzzy logic controller for inverted pendulum-type mobile robot,” J. Daegu Gyeongbuk Stud. 11(3), 119131 (2012).Google Scholar
Ra, J. H., Lim, J. H., Jeon, S. H. and Chung, M. J., “Attitude Control of Segway Using PID Controller,” Proceedings of KSME Spring Conference (2015) pp. 35493552.Google Scholar
Kim, B. W., Hwang, S. J. and Park, B. S., “A Low-Complexity Controller Design for Segway,” Proceedings of KIEE Summer Conference (2015) pp. 13391340.Google Scholar
Yoo, S. J., Jo, J. S., You, S. H. and Lee, K. I., “A VDC Controller Design for Rollover Prevention and Lateral Stability Improvement of Vehicle,” Proceedings of AUTO JOURNAL: Journal of the Korean Society of Automotive Engineers (2006) pp. 766775.Google Scholar
Goher, K. M., Tokhi, M. O. and Siddique, N. H., “Dynamic modeling and control of a two wheeled robotic vehicle with a virtual payload,” J. Eng. Appl. Sci. 6(3), 741 (2011).Google Scholar
Castro, A., Modeling and dynamic analysis of a two-wheeled inverted-pendulum, M.S. Thesis (Georgia Institute of Technology, 2012).Google Scholar
Pratt, G. A. and Williamson, M. M., “Series Elastic Actuators,” Proceedings of IEEE/RSJ International Conference on Intelligent Robot Interaction and Cooperative Robots, PA, USA (1995) pp. 388406.Google Scholar
Liu, L., Wang, X., Jin, L. and Shi, S., “Modeling and Analysis on Effect of Driving Force for Vehicle Stability,” Proceedings of 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE), Changchun, China (2001) pp. 12281231.Google Scholar
Shim, T. and Margolis, D., “An analytical tire model for vehicle simulation in normal driving conditions,” International Journal of Vehicle Design 35(3), 224240 (2004).CrossRefGoogle Scholar
Bakker, E., Pacejka, H. B., and Lidner, L., “A new tire model with an application in vehicle dynamics studies,” SAE Transactions 98(Section 6: JOURNAL OF PASSENGER CARS), 101113 (1989).Google Scholar
Pacejka, H. B. and Bakker, E., “The magic formula tyre model,” Veh. Syst. Dyn. 21(supplement) (1991).Google Scholar
Bakker, E., Nyborg, L. and Pacejka, H. B., “Tyre modeling for use in vehicle dynamics studies,” SAE Technical Paper Series (1987).CrossRefGoogle Scholar
Pacejka, H. B., Tyre and Vehicle Dynamics (Butterworth-Heinemann, 2002).Google Scholar
Guaneri, P., Rocca, G. and Gobbi, M., “A neural-network-based model for the dynamic simulation of the tire/suspension system while traversing road irregularities,” IEEE Trans. Neural Networks 19(9), 15491563 (2008).10.1109/TNN.2008.2000806CrossRefGoogle Scholar
Palkovics, L. and El-Gindy, M., “Neural network representation of tyre characteristics: the neuro-tyre,” Int. J. Veh. Des. 14(5), 56325911 (1993).Google Scholar
Wang, G., Recent Advances in Robotic Systems (InTech, London, 2016).CrossRefGoogle Scholar
Arumugom, S., Muthuraman, S. and Ponselvan, V., “Modeling and application of series elastic actuators for force control multi legged robots,” J. Comput. 1(1), 2633 (2009).Google Scholar
Tumapala, T. S., Saini, S. S., Sarker, U. and Ray, D. D., “Compliance Control of Tele-Robot,” Proceedings of Conference on Advances in Robotics, Mumbai, India (2013) pp. 17.Google Scholar
Oh, S. H. and Kong, K. C., “High-precision robust force control of a series elastic actuator,” IEEE/ASME Trans. Mechatron. 22(1), 7180 (2017).CrossRefGoogle Scholar