Emergency Avoidance Control System for an Automatic Vehicle – Slip Ratio Control Using Sliding Mode Control and Real-Number-Coded Immune Algorithm –

Masafumi Hamaguchi; Takao Taniguchi

doi:10.20965/jrm.2015.p0645

single-rb.php

« previous

JRM Vol.27 No.6 pp. 645-652

doi: 10.20965/jrm.2015.p0645

(2015)

Paper:

Views over last 60 days: 1,319

Emergency Avoidance Control System for an Automatic Vehicle – Slip Ratio Control Using Sliding Mode Control and Real-Number-Coded Immune Algorithm –

Masafumi Hamaguchi and Takao Taniguchi

Interdisciplinary Graduate School of Science and Engineering, Shimane University
1060 Nishikawatsu-cho, Matsue-shi, Shimane 690-8504, Japan

Received:

May 22, 2015

Accepted:

August 20, 2015

Published:

December 20, 2015

Keywords:

immune algorithm, emergency avoidance, sliding mode control, slip ratio control

Abstract

Vehicle behavior in emergency avoidance

The automotive industry facilitates research and development on intelligent transport systems. One area researched intensively to enhance passenger safety is the prevention of collisions by controlling steering and braking precisely. In this study, we assume that an automatic vehicle travelling on a highway is on a collision course with an obstacle. The purpose of this research is combining steering and braking to find a set of operations the vehicle can follow to avoid the projected collision. To do this, we propose slip ratio control using sliding mode control using a real-number-coded immune algorithm (IA). CarSim (produced by Mechanical Simulation Company) provides full vehicle dynamics with 27 degrees of freedom adopted as a vehicle model. Operation waveforms are generated by linear interpolation through designated data points. The IA, which is a coded real-number expression, is used to determine data points. Our proposal's efficiency is verified through emergency avoidance simulation using CarSim. Simulation results demonstrate operation that keeps tires from skidding using slip ratio control and halting the vehicle in the shortest braking distance possible.

Cite this article as:

M. Hamaguchi and T. Taniguchi, “Emergency Avoidance Control System for an Automatic Vehicle – Slip Ratio Control Using Sliding Mode Control and Real-Number-Coded Immune Algorithm –,” J. Robot. Mechatron., Vol.27 No.6, pp. 645-652, 2015.

Data files:

References

[1] R. Matsumi, P. Raksincharoensak, and M. Nagai, “Development of autonomous intelligent driving system to enhance safe and secured traffic society for elderly drivers: Autonomous collision avoidance system with hazard anticipation driver characteristics,” J. of Robotics and Mechatronics, Vol.25, No.6, pp. 966-972, 2013.
[2] R. Matsumi, P. Raksincharoensak, and M. Nagai, “Study on autonomous intelligent drive system based on potential field with hazard anticipation,” J. of Robotics and Mechatronics, Vol.27, No.1, pp. 5-11, 2015.
[3] S. Horiuchi, R. Hirao, K. Okada, and S. Nohtomi, “Optimal steering and braking control in emergency obstacle avoidance,” Trans. of the Japan Society of Mechanical Engineers, Series C, Vol.72, No.722, pp. 180-185, 2006 (in Japanese).
[4] M. Nagai, M. Onda, and T. Katagiri, “Vehicle motion control using genetic algorithm: 2^nd report, emergency avoidance by steering and braking,” Trans. of the Japan Society of Mechanical Engineers, Series C, Vol.62, No.599, pp. 138-144, 1996 (in Japanese).
[5] Y. Xiao, F. Dongmei, and Y. Tao, “Application of artificial immune algorithm in image segmentation based on immune field,” Proc. of the 10^th World Congress on Intelligent Control and Automation (WCICA), pp. 4691-4695, 2012.
[6] M. Kasahara, Y. Kanai, and Y. Mori, “Vehicle braking control using sliding mode control – Switching control for speed and slip ratio –,” Proc. of ICROS-SICE Int. Joint Conf. 2009, pp. 4047-4052, 2009.
[7] Y. Ikeda, T. Nakajima, and Y. Chida, “Vehicular slip ratio control using nonlinear control theory,” J. of System Design and Dynamics, Vol.6, No.2, pp. 145-157, 2012.
[8] K. Higuchi, “Automotive engineering,” Asakura Publishing, 1980 (in Japanese).
[9] Z. Zhang, “Immune optimization algorithm for constrained nonlinear multiobjective optimization problems,” Applied Soft Computing, Vol.7, No.3, pp. 840-857, 2007.
[10] Y. Ishida, “Immunity-based systems: A specification and applications,” Medical Imaging Technology, Vol.18, No.5, pp. 703-708, 2000.
[11] H. Kita, I. Ono, and S. Kobayashi, “Theoretical analysis of the unimodal normal distribution crossover for real-coded genetic algorithms,” Trans. of the Society of Instrument and Control Engineers, Vol.E-2, No.1, pp. 187-194, 2002.
[12] K. Yoshida et al., “Frontiers of motion and vibration control,” Kyoritsu Shuppan Co., Ltd., 2007 (in Japanese).
[13] K. Nonami and H. Tian, “Sliding mode control (Design theory of nonlinear robust control),” Corona Publishing, 1994 (in Japanese).

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] R. Matsumi, P. Raksincharoensak, and M. Nagai, “Development of autonomous intelligent driving system to enhance safe and secured traffic society for elderly drivers: Autonomous collision avoidance system with hazard anticipation driver characteristics,” J. of Robotics and Mechatronics, Vol.25, No.6, pp. 966-972, 2013.

[2] [2] R. Matsumi, P. Raksincharoensak, and M. Nagai, “Study on autonomous intelligent drive system based on potential field with hazard anticipation,” J. of Robotics and Mechatronics, Vol.27, No.1, pp. 5-11, 2015.

[3] [3] S. Horiuchi, R. Hirao, K. Okada, and S. Nohtomi, “Optimal steering and braking control in emergency obstacle avoidance,” Trans. of the Japan Society of Mechanical Engineers, Series C, Vol.72, No.722, pp. 180-185, 2006 (in Japanese).

[4] [4] M. Nagai, M. Onda, and T. Katagiri, “Vehicle motion control using genetic algorithm: 2^nd report, emergency avoidance by steering and braking,” Trans. of the Japan Society of Mechanical Engineers, Series C, Vol.62, No.599, pp. 138-144, 1996 (in Japanese).

[5] [5] Y. Xiao, F. Dongmei, and Y. Tao, “Application of artificial immune algorithm in image segmentation based on immune field,” Proc. of the 10^th World Congress on Intelligent Control and Automation (WCICA), pp. 4691-4695, 2012.

[6] [6] M. Kasahara, Y. Kanai, and Y. Mori, “Vehicle braking control using sliding mode control – Switching control for speed and slip ratio –,” Proc. of ICROS-SICE Int. Joint Conf. 2009, pp. 4047-4052, 2009.

[7] [7] Y. Ikeda, T. Nakajima, and Y. Chida, “Vehicular slip ratio control using nonlinear control theory,” J. of System Design and Dynamics, Vol.6, No.2, pp. 145-157, 2012.

[8] [8] K. Higuchi, “Automotive engineering,” Asakura Publishing, 1980 (in Japanese).

[9] [9] Z. Zhang, “Immune optimization algorithm for constrained nonlinear multiobjective optimization problems,” Applied Soft Computing, Vol.7, No.3, pp. 840-857, 2007.

[10] [10] Y. Ishida, “Immunity-based systems: A specification and applications,” Medical Imaging Technology, Vol.18, No.5, pp. 703-708, 2000.

[11] [11] H. Kita, I. Ono, and S. Kobayashi, “Theoretical analysis of the unimodal normal distribution crossover for real-coded genetic algorithms,” Trans. of the Society of Instrument and Control Engineers, Vol.E-2, No.1, pp. 187-194, 2002.

[12] [12] K. Yoshida et al., “Frontiers of motion and vibration control,” Kyoritsu Shuppan Co., Ltd., 2007 (in Japanese).

[13] [13] K. Nonami and H. Tian, “Sliding mode control (Design theory of nonlinear robust control),” Corona Publishing, 1994 (in Japanese).