JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (5): 57-63.doi: 10.6040/j.issn.1672-3961.0.2017.210

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Fault-tolerant control of autonomous underwater vehicle based on adaptive region tracking

CHU Zhenzhong, ZHU Daqi   

  1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
  • Received:2017-04-25 Online:2017-10-20 Published:2017-04-25

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Abstract: An adaptive region tracking fault-tolerant control for the thrusters of autonomous underwater vehicle was proposed. Different from the traditional fault-tolerant control methods of autonomous underwater vehicle, the region tracking control theory was adopted, and the control target was designed as a spatial region. For the uncertainty and thruster fault in the system, the neural network was used to identify them online. Considering the problem of the divergence of neural network caused by the thrust saturation during the thruster fault, a neural network weight adjustment method based on a saturation factor was proposed. The effectiveness of the proposed method was verified by simulation.

Key words: fault-tolerant control, autonomous underwater vehicle, thrusters, region tracking, adaptive

CLC Number: 

  • TP27
[1] 朱大奇, 刘乾, 胡震. 无人水下机器人可靠性控制技术[J]. 中国造船,2009,50(2):183-192. ZHU Daqi, LIU Qian, HU Zhen. Reliability control technology of unmanned underwater vehicles[J]. Shipbuilding of China, 2009, 50(2):183-192.
[2] CORRADINI M L, CRISTOFARO A. A nonlinear fault-tolerant thruster allocation architecture for underwater remotely operated vehicles[J]. IFAC-PapersOnLine, 2016, 49(23):285-290.
[3] WANG Y, ZHANG M, WILSON P, et al. Adaptive neural network-based backstepping fault tolerant control for underwater vehicles with thrust fault[J]. Ocean Engieering, 2015, 110(1):15-24.
[4] ZHANG M, LIU X, YIN B, et al. Adaptive terminal sliding mode based thruster fault tolerant control for underwater vehicle in time-varying ocean currents[J]. Journal of the Franklin Institute, 2015, 352(11):4935-4961.
[5] ISMAIL Z H, MOKHAR M B M, PUTRANTI V W E, et al. A robust dynamic region-based control scheme for an autonomous underwater vehicle[J]. Ocean Engineering, 2016, 111:155-165.
[6] CHEAH C C, WANG D Q. Region reaching control of robots: theory and experiments[C] // Proceedings of the 2005 IEEE International Conference on Robotics and Automation. [s.l.] :IEEE, 2005:974-979.
[7] LI X, HOU S P, CHEAH C C. Adaptive region tracking control for autonomous underwater vehicle[C] // Proceedings of the 2010 11th International Conference on Control. Automation Robotics & Vision. Singapore: IEEE, 2010:2129-2134.
[8] ISMAIL Z H, DUNNIGAN M W. A region boundary-based control scheme for an autonomous underwater vehicle[J]. Ocean Engineering, 2011, 38(11):2270-2280.
[9] CORRADINI M L, MONTERIU A, ORLANDO G. An actuator failure tolerant control scheme for an underwater remotely operated vehicle[J]. IEEE Transactions on Control Systems Technology, 2011, 19(5):1036-1046.
[10] KIM D W. Tracking of REMUS autonomous underwater vehicles with actuator saturations[J]. Automatica, 2015, 58:15-21.
[11] GAO J, PROCTOR A A, SHI Y, et al. Hierarchical model predictive image-based visual serving of underwater vehicles with adaptive neural network dynamic control[J]. IEEE Transactions on Cybernetics, 2016, 46(10):2323-2334.
[12] 俞建成, 张艾群, 王晓辉,等. 基于模糊神经网络水下机器人直接自适应控制[J]. 自动化学报, 2007, 33(8):840-846. YU Jiancheng, ZHANG Aiqun, WANG Xiaohui, et al. Direct adaptive control of underwater vehicles based on fuzzy neural networks[J]. Acta Automatica Sinica, 2007, 33(8):840-846.
[13] SUN Y S, RAN X R, LI Y M, et al. Thruster fault diagnosis method based on Gaussian particle filter for autonomous underwater vehicles[J]. International Journal of Naval Architecture and Ocean Engineering, 2016, 8(3):243-251.
[14] 张铭钧, 褚振忠. 自主式水下机器人自适应区域跟踪控制[J]. 机械工程学院, 2013, 4(7):148-155. ZHANG Mingjun, CHU Zhenzhong. Adaptive region tracking control for autonomous underwater vehicle[J]. Journal of Mechanical Engineering, 2013, 4(7):148-155.
[15] HUANG X, YAN Y, ZHOU Y. Neural network-based adaptive second order sliding mode control of Lorentz-augmented spacecraft formation[J]. Neurocopution, 2017, 222(26):191-203.
[16] JIA C, LI X, WANG K, et al. Adaptive control of nonlinear system using online error minimum neural networks[J]. ISA Transactions, 2016, 65:125-132.
[17] PODDER T K, SARKAR N. Fault-tolerant control of an autonomous underwater vehicle under thruster redundancy[J]. Robotics and Autonomous Systems, 2001, 34(1):39-52.
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