Actuator fault diagnosis of autonomous underwater vehicle based on improved Elman neural network

Abstract

Autonomous underwater vehicles (AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which is applied to the underwater vehicle motion modeling. Through designing self-feedback connection with fixed gain in the unit connection as well as increasing the feedback of the output layer node, improved Elman network has faster convergence speed and generalization ability. This method for high-order nonlinear system has stronger identification ability. Firstly, the residual is calculated by comparing the output of the underwater vehicle model (estimation in the motion state) with the actual measured values. Secondly, characteristics of the residual are analyzed on the basis of fault judging criteria. Finally, actuator fault diagnosis of the autonomous underwater vehicle is carried out. The results of the simulation experiment show that the method is effective.

Keywords

autonomous underwater vehicle / fault diagnosis / thruster / improved Elman neural network

References

[1] XuY-r, PangY-j, GanY, SunY-shan. AUV-state-of-the-art and prospect [J]. CAAI Transactions on Intelligent Systems, 2006, 1(1): 9-16

[2] SunY-s, WanL, GanY, WangJ-g, JiangC-meng. Design of motion control of dam safety inspection underwater vehicle [J]. Journal of Central South University, 2012, 19(6): 1522-1529

[3] KalwaJ. Underwater inspection using the AUV sea cat [C]. Proceedings of 7th Security Research Conference. Bonn, Germany, 2012311-314

[4] WanL, YangY, LiY-ming. Actuator fault diagnosis of automatic underwater vehicle using gaussian particle filter [J]. Journal of Shanghai Jiao Tong University, 2013, 47(7): 1072-1076

[5] LiuQ, ZhuD-qi. Fault diagnosis and fault tolerant control algoriythm of thrusters of unmanned underwater vehicles [J]. Journal of System simulation, 2010, 22(1): 96-101

[6] ChoiJ K, KondoH, ShimizuE. Thruster fault-tolerant control of a hovering AUV with four horizontal and two vertical thrusters [J]. Advanced Robotics, 2014, 28(4): 245-256

[7] WangL-r, DingKai. Actuator fault diagnosis of autonomous underwater vehicle based on wavelet neural network [J]. Journal of System Simulation, 2007, 19: 206-209

[8] PangY-j, FangS-j, WangL-rong. Thruster fault diagnosis of autonomous underwater vehicle based on least disturbance wavelet neural network [J]. Shipbuilding of China, 2008, 49(2): 94-100

[9] WuJianResearch on multi-faults diagnosis technology of thruster and sensor for AUV [D], 2011HarbinHarbin Engineering University

[10] ZhangM-j, ChunZ-zhong. Thruster fault detection, isolation and reconstruction for autonomous underwater vehicle [J]. Journal of Nanjing University of Aeronautics & Astronautics, 2011, 43: 142-146

[11] ZhuD-q, SunBing. Information fusion fault diagnosis method for unmanned underwater vehicle thrusters [J]. IET Electrical Systems in Transportation, 2013, 3(4): 102-111

[12] AntonelliG, CaccavaleF, SansoneC, VillaniL. Fault diagnosis for AUVs using support vector machines [C]. 2004 IEEE International Conference on Robotics and Automation. New Orleans, USA, 20044486-4491

[13] EdinO, GeoffR. Thruster fault diagnosis and accommodation for open-frame underwater vehicles [J]. Control Engineering Practice, 2004, 12: 1575-1598

[14] CorradiniM L, MonteriuA, OrlandoG. An actuator failure tolerant control scheme for an underwater remotely operated vehicle [J]. IEEE Transactions on Control Systems Technology, 2011, 19(5): 1036-1046

[15] EmitsJ, DeardenR, PebodyM. Automatic fault detection and execution monitoring for AUV missions [J]. IEEE Journal of Oceanic Engineering, Monterey, CA, United states, 20101-10

[16] EzekielJ, LomuscioA, MolnarL. Verifying fault tolerance and self-diagnosability of an autonomous underwater vehicle [C]. 22nd International Joint Conference on Artificial Intelligence. Barcelona, Catalonia, Spain, 20111659-1664