Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-23T21:41:09.025Z Has data issue: false hasContentIssue false
  • English
  • Français

A Neurodynamics Control Strategy for Real-Time Tracking Control of Autonomous Underwater Vehicles

Published online by Cambridge University Press:  29 August 2013

Daqi Zhu ,
Xun Hua  and
Bing Sun
Daqi Zhu*
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University)
Xun Hua
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University)
Bing Sun
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

A biologically inspired neurodynamics-based tracking controller of underactuated Autonomous Underwater Vehicles (AUV) is proposed in this paper. The proposed control strategy includes a velocity controller with biological neurons and an adaptive sliding mode controller. The biological neurons are embedded into the backstepping velocity controller to eliminate the sharp speed jumps commonly existing in vehicles due to tracking errors changing suddenly. The outputs of the velocity controller are used as the command inputs of the sliding mode controller, and the thruster control constraints problems that are commonly seen in the backstepping control of AUV are solved by the proposed controller. Simulation results show that the control strategy achieved success in smoothly tracking AUV position and velocity.

Keywords

AUVTracking controlBiologically inspired neurodynamicsBackstepping control
Type
Research Article
Information
The Journal of Navigation , Volume 67 , Issue 1 , January 2014 , pp. 113 - 127
Copyright
Copyright © The Royal Institute of Navigation 2013 

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

REFERENCES

Antonelli, G., Chiaverini, S., Sarkar, N. and West, M. (2001). Adaptive control of an autonomous underwater vehicle: experimental results on ODIN. IEEE Transactions on Control Systems and Technology, 9, 756–765.CrossRefGoogle Scholar
Bagheri, A., Karimi, T. and Amanifard, N. (2010). Tracking performance control of a cable communicated underwater vehicle using adaptive neural network controllers. Applied Soft Computing, 10, 908–918.CrossRefGoogle Scholar
Bagheri, A. and Moghaddam, J. J. (2009). Simulation and tracking control based on neural-network strategy and sliding-mode control for underwater remotely operated vehicle. Neurocomputing, 72, 1934–1950.CrossRefGoogle Scholar
De Paula, C. F. and Ferreira, L. H. C. (2012). An Improved Analytical PID Controller Design for Non-Monotonic Phase LTI Systems. IEEE Transactions on Control Systems and Technology, 20, 1328–1333.CrossRefGoogle Scholar
Ding, H. and Wang, D. S. (2009). Autonomous Underwater Vehicle Heading Control Based on Sliding Mode Fuzzy Control. Proceedings of the Second International Conference on Modelling and Simulation, Manchester, England, 505–508.Google Scholar
Dongkyoung, C. (2011). Global Tracking Control of Underactuated Ships With Input and Velocity Constraints Using Dynamic Surface Control Method. IEEE Transactions on Control Systems and Technology, 19, 1357–1370.Google Scholar
Edin, O. and Geoff, R. (2004). Thruster fault diagnosis and accommodation for open-frame underwater vehicles. Control Engineering Practice, 12, 1575–1598.Google Scholar
Grossberg, S. (1988). Nonlinear neural networks: Principles, mechanisms, and architecture. Neural Networks, 1, 17–61.CrossRefGoogle Scholar
Hodgkin, A. L. and Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117, 500–544.CrossRefGoogle ScholarPubMed
Ishaque, K., Abdullah, S. S., Ayob, S. M. and Salam, Z. (2010). Single Input Fuzzy Logic Controller for Unmanned Underwater Vehicle. Journal of Intelligent and Robotic Systems, 59, 87–100.CrossRefGoogle Scholar
Ishaque, K., Abdullah, S. S., Ayob, S. M. and Salam, Z. (2011). A simplified approach to design fuzzy logic controller for an underwater vehicle. Ocean Engineering, 38, 271–284.CrossRefGoogle Scholar
Jon, E. R., Asgeir, J. S. and Kristin, Y. P. (2008). Model-Based Output Feedback Control of Slender-Body Underactuated AUVs: Theory and Experiments. IEEE Transactions on Control Systems Technology, 16, 930–946.Google Scholar
Khadija, D., Majda, L. and Said, N. A. (2012). Discrete second order sliding mode control for nonlinear multivariable systems. Proceedings of 16th IEEE Mediterranean Electrotechnical Conference (MELECON), Yasmine Hammamet, 387–390.Google Scholar
Kodogiannis, V. (2003). Direct adaptive control of underwater vehicles using neural networks. Journal of Vibration and Control, 9, 605–619.Google Scholar
Lapierre, L. and Jouvencel, B. (2008). Robust Nonlinear Path-Following Control of an AUV. IEEE Journal of Oceanic Engineering, 33, 89–102.CrossRefGoogle Scholar
Liu, F., Cui, W. C. and Li, X. Y. (2010). China's first deep manned submersible. JIAOLONG, China Earth Science, 53, 1407–1410.CrossRefGoogle Scholar
Luo, C. and Yang, S. X. (2008). A bio-inspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environment. IEEE Transaction on Neural Network, 19, 1279–1298.CrossRefGoogle Scholar
Miller, J., Flowers, G., Bevly, D. (2012). A System for Tracking an Autonomously Controlled Canine. Journal of Navigation, 65, 427–444.CrossRefGoogle Scholar
Pan, M. L., Seok, W. H. and Yong, K. L. (1999). Discrete-time quasi-Sliding mode control of an autonomous underwater vehicle. IEEE Journal of Oceanic Engineering, 24, 388–395.Google Scholar
Pepijn, W. J., Colinvan, V. F. and Daniel, T. (2005). Neural network control of underwater vehicles. Engineering Applications of Artificial Intelligence, 18, 533–547.Google Scholar
Santhakumar, M. and Asokan, T. (2010). Investigations on the Hybrid Tracking Control of an Underactuated Autonomous Underwater Robot. Advanced Robotics, 24, 1529–1556.CrossRefGoogle Scholar
Serdar, S., Bradley, J. B. and Ron, P. P. (2010). Redundancy resolution for underwater mobile manipulators. Ocean Engineering, 37, 325–343.Google Scholar
Sharma, N., Gregory, C. M., Johnson, M. and Dixon, W. E. (2012). Closed-Loop Neural Network-Based NMES Control for Human Limb Tracking. IEEE Transactions on Control Systems and Technology, 20, 712–725.CrossRefGoogle Scholar
Slotine, J. J. E. and Li, W. (1991). Applied nonlinear control. New Jersey: Prentice Hall.Google Scholar
Tsai, P. S., Wang, L. S. and Chang, F. R. (2004). Systematic backstepping design for b-spline trajectory tracking control of the mobile robot in hierarchical model. IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan, 713–718.Google Scholar
Wai, R. J. (2007). Fuzzy sliding-mode control using adaptive tuning technique. IEEE Transactions on Industrial Electronics, 54, 586–594.CrossRefGoogle Scholar
Wallace, M. B., Max, S.D. and Edwin, K. (2008). Depth control of remotely operated underwater vehicles using an adaptive fuzzy sliding mode controller. Robotics and Autonomous Systems, 56, 670–677.Google Scholar
Yang, S. X. and Luo, C. (2004). A neural network approach to complete coverage path planning, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 34, 718–725.CrossRefGoogle ScholarPubMed
Zhang, L. J., Qi, X. and Pang, Y. J. (2009). Adaptive output feedback control based on DRFNN for AUV. Ocean Engineering, 36, 716–722.CrossRefGoogle Scholar
Zhang, Y.L., Velinsky, S. A. and Feng, X. (1997). On the tracking control of differentially steered wheeled mobile robots. Journal of Dynamic Systems, Measurement and Control, 119, 455–461.CrossRefGoogle Scholar
Zheng, H., Wang, H.B. and Wu, L. (2013). Simulation Research on Gravity-Geomagnetism Combined Aided Underwater Navigation. Journal of Navigation, 66, 83–98.CrossRefGoogle Scholar
Zhu, D.Q., Liu, Q. and Hu, Z. (2011). Fault-tolerant control algorithm of the manned submarine with multi-thruster based on quantum-behaved particle swarm optimization. International Journal of Control, 84, 1817–1829.CrossRefGoogle Scholar