OPTIMIZED FUZZY CONTROL DESIGN OF AN AUTONOMOUS UNDERWATER VEHICLE

Document Type : Research Paper

Authors

1 School of Electrical and Computer Engineering, Shiraz Univer- sity, Shiraz, Iran and Iranian Space Agency, Iranian Space Center, Mechanic Institute, Shiraz, Iran, P.O. Box: 71555-414

2 School of Electrical and Computer Engineering, Shiraz Univer- sity, Shiraz, Iran

3 School of Electrical and Computer Engineering, Shiraz Uni- versity, Shiraz, Iran

Abstract

In this study, the roll, yaw and depth fuzzy control of an Au-
tonomous Underwater Vehicle (AUV) are addressed. Yaw and roll angles are
regulated only using their errors and rates, but due to the complexity of depth
dynamic channel, additional pitch rate quantity is used to improve the depth
loop performance. The discussed AUV has four aps at the rear of the vehicle
as actuators. Two rule bases and membership functions based on Mamdani
type and Sugeno type fuzzy rule have been chosen in each loop. By invoking
the normalized steepest descent optimization method, the optimum values for
the membership function parameters are found. Though the AUV is a highly
nonlinear system, the simulation of the designed fuzzy logic control system
based on the equations of motion shows desirable behavior of the AUV spe-
cially when the parameters of the fuzzy membership functions are optimized.

Keywords

References

[1] G. Antonelli, S. Chiaverini, N. Sarkar and M. West, Adaptive control of an autonomous
underwater vehicle. experimental results on ODIN, IEEE Proceedings, International Sympo-
sium on Computational Intelligence in Robotics and Automation, 1999.
[2] A. Balasuriya and L. Cong, Adaptive fuzzy sliding mode controller for underwater vehicles,
IEEE Proceedings, The 4th international conference on control and automations (ICCA'03),
Canada, June 2003.
[3] T. BinaZadeh, A. R. Khayatian and P. KarimAghaee, Identification and control of 6 DOF underwater
variable mass object, 13th iranian conference on electrical engineering (ICEE2005),
Zanjan, Iran, 2005.
[4] J. Blakelock, Automatic control of aircraft and missiles, 2nd edition, Willy, February 1991.
[5] F. Dougherty, T. Sherman, G. Woolweaver and G. Lovell, An autonomous underwater vehicle
(AUV) flight control system using sliding mode control, Proceedings, OCEANS '88.
Baltimore, MD USA, Oct. 1988.
[6] T. Fossen and M. Blanke, Nonlinear output feedback control of underwater vehicle propellers
using feedback from estimated axial flow velocity, IEEE Journal of Oceanic Engineering, Apr
2000.
[7] T. Fossen, Guidance and control of ocean vehicles, John Wiley & Sons, 1994.
[8] J. S. Han, H. S. Kim and J. Neggers, Actions, norms, subactions and kernels of (fuzzy)
norms, Iranian Journal of Fuzzy Systems, 7(2) (2010), 141-147.
[9] A. Hasankhani, A. Nazari and M. Sahelis, Some properties of fuzzy hilbert spaces and norm
of operators, Iranian Journal of Fuzzy Systems, 7(3) (2010), 129-157.
[10] K. Ishii and T. Ura, An adaptive neural-net controller system for an underwater vehicle,
Control Engineering Practice, Elsevierm, 8(2) (2000), 177-184.
[11] J. S. R. Jang, C. T. Sun and E. Mizutani, Neuro-fuzzy and soft computing, Prentic Hall,
1997.
[12] N. E. Leonard and P. S. Krishnaprasad, Motion control of an autonomous underwater vehicle
with an adaptive feature, IEEE Proceedings of Autonomous Underwater Vehicle Technology,
AUV '94, Cambridge, MA, USA, Jul 1994.
[13] J. H. Li, P. M. Lee and S. J. Lee, Neural net based nonlinear daptive control for autonomous
underwater vehicles, IEEE international Conference on Robotics and Automation, May 2002.
[14] Y. Nakamura and S. Savant, Nonlinear tracking control of autonomous underwater vehicles,
IEEE Proceedings on Robotics and Automation, May 1992.
[15] T. Prestero, Development of a six-degree of freedom simulation model for the REMUS autonomous
underwater vehicle, MTS/IEEE Conference and Exhibition, OCEANS, 2001.
[16] B. Raeisy, M. Kharati, A. A. Safavi and A. R. Khayatian, Equation of motion derivation of
variable mass underwater vehicle and 6DOF simulation with helping of neural network, 17th
Anual International Conference on Mechanical Engineering, Tehran, Iran, May 2009.
[17] B. Raeisy, A. A. Safavi and A. R. Khayatian, Fuzzy logic depth control of an autonomous
underwater vehicle and optimization of it with normalize steepened descent method, 17th
Anual International Conference on Mechanical Engineering, Tehran, Iran, May 2009.
[18] B. Raeisy, A. A. Safavi and A. R. Khayatian, Optimized fuzzy logic yaw and roll control of
an autonomous underwater vehicle, 8th Iranian Conference on Fuzzy System, Tehran, Iran,
October 2008.
[19] L. Rodrigues and P.Tavares, Sliding mode control of an AUV in the diving and steering
planes, MTS/IEEE Conference Proceedings, MG de Sousa Prado- OCEANS'96, Sep 1996.
[20] N. Sey'edi and M. A. Mirjalili, Hydrodynamic stability coefficients calculation of submarine
and its weapons using added mass and misile DATCOM, 4th Conference of Underwater
Science and Technology (fcoust), Isfahan, May 2007.
[21] E. Shivanian and E. Khoram, Optimization of linear objective function subject to fuzzy relation
inequalities constraints with max-product compozition, Iranian Journal of Fuzzy Systems,
7(3) (2010), 51-71.
[22] S. R.Vukelich, S. L. Stoy and M. E. Moore, Missile DATCOM user’s manual, Dought Aircraft
Company Inc., 1988.
[23] J. Wang and G. Lee, Self-adaptive recurrent neuro-fuzzy control of an autonomous underwater
vehicle, IEEE Transactions on Robotics and Automation, 19(2) (2003).
Iranian Journal of Fuzzy Systems
Volume 9, Issue 2 - Serial Number 2
May and June 2012
Pages 25-41

Files

Share

How to cite

Statistics