Sliding Mode Control of Autonomous Underwater Vehicles
This paper describes a sliding mode controller for
autonomous underwater vehicles (AUVs). The dynamic of AUV
model is highly nonlinear because of many factors, such as
hydrodynamic drag, damping, and lift forces, Coriolis and centripetal
forces, gravity and buoyancy forces, as well as forces from thruster.
To address these difficulties, a nonlinear sliding mode controller is
designed to approximate the nonlinear dynamics of AUV and
improve trajectory tracking. Moreover, the proposed controller can
profoundly attenuate the effects of uncertainties and external
disturbances in the closed-loop system. Using the Lyapunov theory
the boundedness of AUV tracking errors and the stability of the
proposed control system are also guaranteed. Numerical simulation
studies of an AUV are included to illustrate the effectiveness of the
presented approach.
[1] Xu Yuru, Xiao Kun, “Technology development of autonomous ocean
vehicle”, Journal of Automation, vol. 33, no. 5, pp.518-521, 2007.
[2] Xu Yuru, Pang Yongjie, Wan Lei, Sun Yushan, “AUV--state-of-the-art
and prospect”, CAAI Transactions on Intelligent Systems, vol. 1, no. 1,
pp.9-16, 2006.
[3] Yang Shi1. Weiqi Qian. “Adaptive Depth Control for Autonomous
Underwater.Vehicles Based on Feedforward Neural Networks’’. Vol. 4
Issue 3, pp 107-118,2007.
[4] A. J. Healey and D. Lienard, “Multivariable sliding mode control for
autonomous diving and steering of unmanned underwater vehicles,”
IEEE Journal of Oceanic Engineering, vol. 18, no. 3, pp. 327-339, 1993.
[5] D. Xu, W. Yan, and Y. Shi, “Nonlinear variable structure double mode
control of autonomous underwater vehicles,” Proc. IEEE International
Symposium on Underwater Technology, pp. 425-430, Tokyo, May 23-26,
2000.
[6] Y. Nakamura, and S. Savant, “Nonlinear tracking control of autonomous
underwater vehicles,” Proc. IEEE Int. Conf. on Robotics and
Automation, vol. 3, pp. A4-A9, 1992.
[7] J. Nie, J. Yuh, E. Kardash, and T. I. Fossen, “Onboard sensor-based
adaptive control of small UUVs in the very shallow water,” Proc. IFACControl
Applications in Marine Systems, Fukuoka, Japan, pp. 201-206,
1998.
[8] N. Kato, “Applications of fuzzy algorithm to guidance and control of
underwater vehicles,” Underwater Robotic Vehicles: Design and
Control, J. Yuh (Ed.), TSI: Albuquerque, 1995.
[9] Huanan Yu, Jun Dai, “Control of autonomous underwater vehicle using
fuzzy logic tuned by genetic algorithm” Journal of Harbin Engineering
University, vol. 23, no. 5, pp.12-15, 2002.
[10] F.L.Lewis, S.Jagannathan and A. Yesildirek , “Neural Network Control
of Robot Manipulators and Nonlinear systems”, Taylor & Francis ,
1999.
[11] Ahmad Forouzantabar, Ahmadreza khoogar, mohammad javade
fakharzadegan, “Controlling a New Biped Robot Model Since Walking
Using Neural Network”,IEEE ICIT Conference, March 2007,Shenzhen,
China.
[12] Xiao Liang, Yong Gan, Lei Wan, “Motion Controller for Autonomous
Underwater Vehicle Based on Parallel Neural Network’’, International
Journal of Digital Content Technology and its Applications, Vol 4, no 9,
2010.
[13] Ji-Hong Li, Pan-Mook Lee, Sang-Jeong Lee, “Motion control of an
AUV using a neural network adaptive controller’’. Proceedings of the
2002 International Symposium on Underwater Technology, p p.217-221,
2002.
[14] A Forouzantabar, B Gholami, M Azadi, “Adaptive Neural Network
Control of Autonomous Underwater Vehicles’’, International journal of
Aerospace and Mechanical Engineering, issue 67, p: 304-309, 2012.
[15] T. Fossen. Guidance and Control of Ocean Vehicles. John Wiley and
Sons, United States of America, 1995.
[16] J. Yuh. Modeling and control of underwater robot vehicles. In IEEE
Transactions on Systems, Man and Cybernetics, volume 20, pages 1475–
1483, 1990.
[17] P. Ridao et al. Model identification of a low-speed uuv. In Control
Applications in Marine Systems. International Federation on Automatic
Control, 2001.
[18] Ahmad Forouzantabar, Ahmadreza khoogar, Ahmad Reza Vali, “Neural
Network Control of a New Biped Robot Model with Back Propagation
Algorithm”, IEEE RO-MAN 2007, August 26-29, Korea.
[19] Gonzalez, L.A. Design, modeling and control of an autonomous
underwater vehicle). Bachelor of Engineering Honours Thesis. The
University of Western Australia. 2004.
[1] Xu Yuru, Xiao Kun, “Technology development of autonomous ocean
vehicle”, Journal of Automation, vol. 33, no. 5, pp.518-521, 2007.
[2] Xu Yuru, Pang Yongjie, Wan Lei, Sun Yushan, “AUV--state-of-the-art
and prospect”, CAAI Transactions on Intelligent Systems, vol. 1, no. 1,
pp.9-16, 2006.
[3] Yang Shi1. Weiqi Qian. “Adaptive Depth Control for Autonomous
Underwater.Vehicles Based on Feedforward Neural Networks’’. Vol. 4
Issue 3, pp 107-118,2007.
[4] A. J. Healey and D. Lienard, “Multivariable sliding mode control for
autonomous diving and steering of unmanned underwater vehicles,”
IEEE Journal of Oceanic Engineering, vol. 18, no. 3, pp. 327-339, 1993.
[5] D. Xu, W. Yan, and Y. Shi, “Nonlinear variable structure double mode
control of autonomous underwater vehicles,” Proc. IEEE International
Symposium on Underwater Technology, pp. 425-430, Tokyo, May 23-26,
2000.
[6] Y. Nakamura, and S. Savant, “Nonlinear tracking control of autonomous
underwater vehicles,” Proc. IEEE Int. Conf. on Robotics and
Automation, vol. 3, pp. A4-A9, 1992.
[7] J. Nie, J. Yuh, E. Kardash, and T. I. Fossen, “Onboard sensor-based
adaptive control of small UUVs in the very shallow water,” Proc. IFACControl
Applications in Marine Systems, Fukuoka, Japan, pp. 201-206,
1998.
[8] N. Kato, “Applications of fuzzy algorithm to guidance and control of
underwater vehicles,” Underwater Robotic Vehicles: Design and
Control, J. Yuh (Ed.), TSI: Albuquerque, 1995.
[9] Huanan Yu, Jun Dai, “Control of autonomous underwater vehicle using
fuzzy logic tuned by genetic algorithm” Journal of Harbin Engineering
University, vol. 23, no. 5, pp.12-15, 2002.
[10] F.L.Lewis, S.Jagannathan and A. Yesildirek , “Neural Network Control
of Robot Manipulators and Nonlinear systems”, Taylor & Francis ,
1999.
[11] Ahmad Forouzantabar, Ahmadreza khoogar, mohammad javade
fakharzadegan, “Controlling a New Biped Robot Model Since Walking
Using Neural Network”,IEEE ICIT Conference, March 2007,Shenzhen,
China.
[12] Xiao Liang, Yong Gan, Lei Wan, “Motion Controller for Autonomous
Underwater Vehicle Based on Parallel Neural Network’’, International
Journal of Digital Content Technology and its Applications, Vol 4, no 9,
2010.
[13] Ji-Hong Li, Pan-Mook Lee, Sang-Jeong Lee, “Motion control of an
AUV using a neural network adaptive controller’’. Proceedings of the
2002 International Symposium on Underwater Technology, p p.217-221,
2002.
[14] A Forouzantabar, B Gholami, M Azadi, “Adaptive Neural Network
Control of Autonomous Underwater Vehicles’’, International journal of
Aerospace and Mechanical Engineering, issue 67, p: 304-309, 2012.
[15] T. Fossen. Guidance and Control of Ocean Vehicles. John Wiley and
Sons, United States of America, 1995.
[16] J. Yuh. Modeling and control of underwater robot vehicles. In IEEE
Transactions on Systems, Man and Cybernetics, volume 20, pages 1475–
1483, 1990.
[17] P. Ridao et al. Model identification of a low-speed uuv. In Control
Applications in Marine Systems. International Federation on Automatic
Control, 2001.
[18] Ahmad Forouzantabar, Ahmadreza khoogar, Ahmad Reza Vali, “Neural
Network Control of a New Biped Robot Model with Back Propagation
Algorithm”, IEEE RO-MAN 2007, August 26-29, Korea.
[19] Gonzalez, L.A. Design, modeling and control of an autonomous
underwater vehicle). Bachelor of Engineering Honours Thesis. The
University of Western Australia. 2004.
@article{"International Journal of Information, Control and Computer Sciences:71087", author = "Ahmad Forouzan Tabar and Mohammad Azadi and Alireza Alesaadi", title = "Sliding Mode Control of Autonomous Underwater Vehicles", abstract = "This paper describes a sliding mode controller for
autonomous underwater vehicles (AUVs). The dynamic of AUV
model is highly nonlinear because of many factors, such as
hydrodynamic drag, damping, and lift forces, Coriolis and centripetal
forces, gravity and buoyancy forces, as well as forces from thruster.
To address these difficulties, a nonlinear sliding mode controller is
designed to approximate the nonlinear dynamics of AUV and
improve trajectory tracking. Moreover, the proposed controller can
profoundly attenuate the effects of uncertainties and external
disturbances in the closed-loop system. Using the Lyapunov theory
the boundedness of AUV tracking errors and the stability of the
proposed control system are also guaranteed. Numerical simulation
studies of an AUV are included to illustrate the effectiveness of the
presented approach.
", keywords = "Lyapunov stability, autonomous underwater vehicle (AUV), sliding mode controller, electronics engineering.", volume = "8", number = "3", pages = "546-4", }
