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Fault-tolerant control algorithm of the manned submarine with multi-thruster based on quantum-behaved particle swarm optimisation. (English) Zbl 1236.93112

Summary: A thruster reconfiguration control approach of manned submarine with 7000 m operation depth based on Quantum-behaved Particle Swarm Optimization (QPSO) is presented in this article. The manned submarine has eight thrusters. When thruster faults happen, the corresponding weight matrix is updated to restrict the usage of the faulty thruster. But the solution with this method may become unfeasible (exceed the rated valve of the thruster) and cannot be directly applied to the thrusters. In order to complete an appropriate control law reconfiguration, a novel control reallocation method based on QPSO is proposed. Not only the solution is obtained by the approach of QPSO limited in the whole feasible space, but also the control error of the fault-tolerant control is very small. Eight dimensions of the particles are used in this article, and each particle represents the components of the control vector, it searches in the range of the restricted factor value to make sure that all the reconfiguration control solutions are feasible. Compared to the weighted pseudo-inverse method, the error of the obtained control vector with the QPSO is very small. Finally, simulation examples of multi-uncertain faults are given to illustrate the advantages of the proposed method.

MSC:

93C85 Automated systems (robots, etc.) in control theory
90B25 Reliability, availability, maintenance, inspection in operations research
76B75 Flow control and optimization for incompressible inviscid fluids
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References:

[1] DOI: 10.1080/00207170500241817 · Zbl 1099.93039 · doi:10.1080/00207170500241817
[2] DOI: 10.1007/s11047-007-9050-z · Zbl 1148.68375 · doi:10.1007/s11047-007-9050-z
[3] Bergh, V.D. (2001), ’Training Product Unit Networks using Cooperative Particle Swarm Optimization’,Proceedings of the International Joint Conference on Neural Networks, 1, 126–131
[4] Blanke M, Diagnosis and Fault-Tolerant Control (2003)
[5] DOI: 10.1109/4235.985692 · doi:10.1109/4235.985692
[6] Cuadrado, AA. and Diaz, I. (2001), ’Fuzzy Inference Map for Condition Monitoring with Self-organizing Maps’, inProceedings of the International Conference in Fuzzy Logic and Technology, Leicester, UK, 5–7 September, pp. 55–58
[7] Doucet A, Sequential Monte Carlo Methods in Practice (Information Science and Statistics) (2010)
[8] DOI: 10.1016/j.conengprac.2003.12.014 · doi:10.1016/j.conengprac.2003.12.014
[9] Fossen TI, Guidance and Control of Ocean Vehicles (1995)
[10] Gao XZ, International Journal of Innovative Computing, Information and Control 6 pp 4235– (2010)
[11] Gianluca, A. 2006.Underwater Robots Motion and Force Control,, 2nd, 78–93. Heidelberg, Berlin: Springer-Verlag.
[12] Kennedy J, Swarm Intelligence (2001)
[13] DOI: 10.1109/TSMCB.2006.881905 · doi:10.1109/TSMCB.2006.881905
[14] Mahmoud MM, Active Fault Tolerant Control Systems: Stochastic Analysis and Synthesis (2003)
[15] DOI: 10.1016/j.rser.2009.03.007 · doi:10.1016/j.rser.2009.03.007
[16] Margarita RS, International Journal of Computational Intelligence Research 2 pp 287– (2006)
[17] Negnevitsky M, Artificial Intelligence: A Guide to Intelligent System,, 2. ed. (2004)
[18] Noura H, Fault-tolerant Control Systems: Design and Practical Applications (Advances in Industrial Control) (2009)
[19] DOI: 10.1016/j.conengprac.2003.12.014 · doi:10.1016/j.conengprac.2003.12.014
[20] Omerdic, E. Roberts, G.N., and Ridao, P. (2003), ’Fault Detection and Accommodation for ROVs’, inProceedings of the Sixth IFAC Conference on Manoeuvring and Control of Marine Craft, MCMC, Girona, Spain, 17–19 September, pp. 575–588
[21] Omerdic, E. Roberts, G.N., and Toal, D. (2004), ’Extension of Feasible Region of Control Allocation for Open-frame Underwater Vehicles’, inProceedings of the IFAC Conference on Control Applications in Marine Systems, Ancona, Italy, 7–9 July, pp. 23–35
[22] Patton, RJ. (1997), ’Fault-tolerant Control: The 1997 Situation (Survey)’, inProceedings of the IFAC Symposium on Fault Detection, Supervision, and Safety for Technical Processes, Hull, England, 26–28 August, pp. 1029–1052
[23] Podder, TK. Antonelli, G., and Sarkar, N. (2000), ’Fault Tolerant Control of an Autonomous Underwater Vehicle under Thruster Redundancy: Simulation and Experiments’, inProceedings of the IEEE International Conference on Robotics and Automation, Vol. 2, San Francisco, CA, 24–28 April, pp. 276–285
[24] Podder, TK. Antonelli, G., and Sarkar, N. (2000), ’Fault Tolerant Control of an Autonomous Underwater Vehicle under Thruster Redundancy: Simulations and Experiments’,IEEE International Conference on Robotics and Automation, Vol. 4, San Francisco, CA, 24–28 April, pp. 1251–1256
[25] Podder, TK. and Sarkar, N. (1999), ’Fault-tolerant Decomposition of Thruster Forces of an Autonomous Underwater Vehicle’, inProceedings of the IEEE International Conference on Robotics and Automation, Vol. 1, Detroit, MI, 10–15 May, pp. 84–89
[26] DOI: 10.1016/S0921-8890(00)00100-7 · doi:10.1016/S0921-8890(00)00100-7
[27] DOI: 10.1007/s11721-007-0002-0 · doi:10.1007/s11721-007-0002-0
[28] Shi, Y. and Eberhart, R. (1999), ’Empirical Study of Particle Swarm Optimization’, inProceedings of the Congress on Evolutionary Computation, Washington DC, 6–9 July, pp. 1945–1950
[29] Sivanandam SN, Introduction to Genetic Algorithms (2010)
[30] Sun, J. Feng, B., and Xu, W.B. (2004), ’Particle Swarm Optimization with Particles having Quantum Behavior’, inProceedings of the IEEE Congress on Evolutionary Computation, Vol. 1, Portland, OR, 19–23 June, pp. 325–331
[31] Sun, J. Feng, B., and Xu, W.B. (2005), ’Adaptive Parameter Control for Quantum-behaved Particle Swarm Optimization on Individual Level’, inProceedings of the 2005 IEEE International Conference on Systems, Man and Cybernetics, Piscataway, NJ, 10–12 October, pp. 3049–3054
[32] DOI: 10.1016/S0005-1098(02)00306-0 · Zbl 1022.93018 · doi:10.1016/S0005-1098(02)00306-0
[33] DOI: 10.1080/002077299291877 · Zbl 0994.93512 · doi:10.1080/002077299291877
[34] Yang, KC and Yuh, J. and Choi, S.K. (1998), ’Experimental Study of Fault-tolerant System Design for Underwater Robots’, inProceedings of the IEEE International Conference on Robotics and Automation, Leuven, Belgium, 16–20 May, pp. 1051–1056
[35] Yoerger, DG. and Slotine, J.J.E. (1991), ’Adaptive Sliding Control of an Experimental Underwater Vehicle’, inProceedings of the IEEE International Conference on Robotics and Automation, Sacramento, CA, 9–11 April, pp. 2746–2751
[36] DOI: 10.1016/j.arcontrol.2008.03.008 · doi:10.1016/j.arcontrol.2008.03.008
[37] Yu JC, ROBOT 28 pp 519– (2006)
[38] Zhu DQ, International Journal of Information Technology 12 pp 1– (2006)
[39] DOI: 10.1080/00207170701441877 · Zbl 1194.93111 · doi:10.1080/00207170701441877
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