Bi-criteria Optimal Fault-Tolerable Control for SY-II Remote Operated Vehicle

doi:10.1007/s12204-013-1438-6

Journal of shanghai Jiaotong University (Science) ›› 2013, Vol. 18 ›› Issue (5): 542-548.doi: 10.1007/s12204-013-1438-6

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Bi-criteria Optimal Fault-Tolerable Control for SY-II Remote Operated Vehicle

JIANG Shu-qiang1* (姜述强), JIN Hong-zhang1 (金鸿章), WEI Feng-mei2,3 (魏凤梅)

(1. College of Automation, Harbin Engineering University, Harbin 150001, China; 2. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China; 3. School of Technology, Harbin University, Harbin 150086, China)
(1. College of Automation, Harbin Engineering University, Harbin 150001, China; 2. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China; 3. School of Technology, Harbin University, Harbin 150086, China)

Online:2013-10-31 Published:2013-12-05
Contact: JIANG Shu-qiang (姜述强) E-mail:jiangshuqiang@hrbeu.edu.cn

Abstract

Abstract: This paper proposed a bi-criteria weighting approach for fault tolerant control (FTC) of SY-II remote operated vehicle (ROV). This approach integrates the minimum kinetic energy (2-norm optimal) approach with the infinity-norm approach through a weighting coefficient, on the basis of SY-II ROV force allocation model. For the realization of fault tolerable control, this approach converts a quadratic programming problem into primaldual neural network. From the motion control simulations and experiments, bi-criteria optimization approach outperforms minimum kinetic energy optimization in FTC, SY-II ROV can realize 2-degree of freedom (DOF) horizontal fault tolerant control with one main thruster and any of horizontal ones. Therefore, this scheme is proved to be of superiority and computational efficiency, both the reliability and safety for ROV have been improved.

Key words: remote operated vehicle (ROV)| bi-criteria control| quadratic programming| fault tolerance control(FTC)

摘要： This paper proposed a bi-criteria weighting approach for fault tolerant control (FTC) of SY-II remote operated vehicle (ROV). This approach integrates the minimum kinetic energy (2-norm optimal) approach with the infinity-norm approach through a weighting coefficient, on the basis of SY-II ROV force allocation model. For the realization of fault tolerable control, this approach converts a quadratic programming problem into primaldual neural network. From the motion control simulations and experiments, bi-criteria optimization approach outperforms minimum kinetic energy optimization in FTC, SY-II ROV can realize 2-degree of freedom (DOF) horizontal fault tolerant control with one main thruster and any of horizontal ones. Therefore, this scheme is proved to be of superiority and computational efficiency, both the reliability and safety for ROV have been improved.

关键词: remote operated vehicle (ROV)| bi-criteria control| quadratic programming| fault tolerance control(FTC)

CLC Number:

TP 242.6

JIANG Shu-qiang1* (姜述强), JIN Hong-zhang1 (金鸿章), WEI Feng-mei2,3 (魏凤梅). Bi-criteria Optimal Fault-Tolerable Control for SY-II Remote Operated Vehicle[J]. Journal of shanghai Jiaotong University (Science), 2013, 18(5): 542-548.

References

[1] Corradini M L, Montei`u A, Orlando G. An actuator failure tolerant control scheme for an underwater remotely operated vehicle [J]. IEEE Transactions on Control Systems Technology, 2001, 19(3): 1036-1046.
[2] Xu Y R, Xiao K. Technology development of autonomous ocean vehicle [J]. Acta Automatica Sinica, 2007, 33(5): 518-521.
[3] Shumsky A, Zhirabok A, Hajiyev C. Observer based fault diagnosis in thrusters of autonomous underwater vehicle [C]// 2010 Conference on Control and Fault Tolerant Systems. Nice, France: IEEE Computer Society, 2010: 11-16.
[4] Boskovic J, Mehra R. A decentralized scheme for accommodation of multiple simultaneous actuator failures [C]// Proceedings of American Control Conference. Anchorage, Alaska, USA: IEEE, 2002: 5098-5103.
[5] Veillette R J, Meadanic J V, Perkin W R. Design of reliable control systems [J]. IEEE Transactions on Automatic Control, 1992, 37(3): 290-304.
[6] Yang L P, Zhang M J, Chu Z Z, et al. Anti-windup control and active fault tolerant control methods for autonomous underwater vehicles [J]. Journal of Harbin Engineering University, 2010, 6(31): 755-760.
[7] Veillete R J. Reliable linear-quadratic statefeedback control [J]. Automatica, 1995, 31(1): 137-143.
[8] Tang X J, Xie L, Ren Z, et al. Integrated design of fault tolerant control system based on fault tolerant observer [J]. Journal of Northwestern Polytechnical University, 2001, 19(2): 313-316.
[9] Yu J C, Zhang A Q, Wang X H. Research on thruster fault tolerant control allocation of a 7000 m manned submarine [J]. Robot, 2006, 28(5): 519-524.
[10] Edin O, Geoff R. Thruster fault diagnosis and accommodation for open-frame underwater vehicles [J]. Control Engineering Practice, 2004, 12(2): 1575-1598.
[11] 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.
[12] Zhu D Q, Liu Q, Yang Y S. An active faulttolerant control method of unmanned underwater vehicles with continuous and uncertain faults [J]. International Journal of Advanced Robotics Systems, 2008, 5(4): 411-418.
[13] Zhang Y. A set of nonlinear equations and inequalities arising in robotics and its online solution via a primal neural network [J]. Neurocomputing, 2006, 70(12): 513-524.
[14] Sarkar N, Podder T K, Antoelli G. Fault accommodating thruster force allocation of an AUV considering thruster redundancy and saturation [J]. IEEE Transactions on Robotics and Automation, 2002, 18(2): 223-231.
[15] Serdar S, Bradley J B, Ron P P. A chattering-free sliding-mode controller for underwater vehicles with fault tolerant infinity-norm thrust allocation [J]. Ocean Engineering, 2008, 35: 1647-1659.
[16] Bazarra M S, Sherali H D, Shetty C M. Nonlinear programming-theory and algorithms [M]. New York: Wiley, 2006.
[17] Xia Y, Wang J. A recurrent neural network for solving linear projection equations [J]. Neural Networks, 2000, 13(3): 337-350.
[18] He B, Yang H. A neural network model for monotone linear asymmetric variational inequalities [J]. IEEE Transactions on Neural Networks, 2000, 11(1): 3-16.

Bi-criteria Optimal Fault-Tolerable Control for SY-II Remote Operated Vehicle

Bi-criteria Optimal Fault-Tolerable Control for SY-II Remote Operated Vehicle

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