Hybrid robust fault detection and isolation of satellite reaction wheel actuators

References

• Ahdesmäki, M., & Strimmer, K. (2010). Feature selection in omics prediction problems using cat scores and false nondiscovery rate control. The Annals of Applied Statistics, 4(1), 503–519. https://doi.org/10.1214/09-AOAS277.
   Web of Science ®Google Scholar
• Alwi, H., Edwards, C., & Marcos, A. (2010, October). FDI for a Mars orbiting satellite based on a sliding mode observer scheme. In 2010 Conference on control and fault-tolerant systems (SysTol) (pp. 125–130). IEEE.
   Google Scholar
• Andreas, T., & Jahrer, M. (2009). The Big Chaos solution to the Netix grand prize (Technical report).
   Google Scholar
• Baldi, P., Blanke, M., Castaldi, P., Mimmo, N., & Simani, S. (2019). Fault diagnosis for satellite sensors and actuators using nonlinear geometric approach and adaptive observers. International Journal of Robust and Nonlinear Control, 29(16), 5429–5455. https://doi.org/10.1002/rnc.v29.16
   Web of Science ®Google Scholar
• Baldi, P., Castaldi, P., Mimmo, N., & Simani, S. (2013, October). Satellite attitude active FTC based on geometric approach and RBF neural network. In 2013 Conference on control and fault-tolerant systems (SysTol) (pp. 667–673). IEEE.
   Google Scholar
• Baldi, P., Castaldi, P., Mimmo, N., & Simani, S. (2014). A new aerodynamic decoupled frequential FDIR methodology for satellite actuator faults. International Journal of Adaptive Control and Signal Processing, 28(9), 812–832. https://doi.org/10.1002/acs.v28.9
   Web of Science ®Google Scholar
• Baldi, P., Castaldi, P., Mimmo, N., & Simani, S. (2017). Combined singular perturbations and nonlinear geometric approach to FDI in satellite actuators and sensors. IFAC-PapersOnLine, 50(1), 7253–7259. https://doi.org/10.1016/j.ifacol.2017.08.1375
   Google Scholar
• Barua, A., Sinha, P., & Khorasani, K. (2009). A diagnostic tree approach for fault cause identification in the attitude control subsystem of satellites. IEEE Transactions on Aerospace and Electronic Systems, 45(3), 983–1002. https://doi.org/10.1109/TAES.2009.5259178
   Web of Science ®Google Scholar
• Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (No. 4, Vol. 4, p. 738). Springer.
   Google Scholar
• Boskovic, J. D., Bergstrom, S. E., & Mehra, R. K. (2005). Robust integrated flight control design under failures, damage, and state-dependent disturbances. Journal of Guidance, Control, and Dynamics, 28(5), 902–917. https://doi.org/10.2514/1.11272
   Web of Science ®Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Calhoun, P. C., & Garrick, J. C. (2007, September). Observing mode attitude controller for the lunar reconnaissance orbiter. In Proceedings of the 20th international symposium on space flight dynamics.
   Google Scholar
• Chen, W., & Saif, M. (2007). Observer-based fault diagnosis of satellite systems subject to time-Varying thruster faults. ASME Journal of Dynamic Systems, Measurement and Control, 129(3), 352–356. https://doi.org/10.1115/1.2719773
   Web of Science ®Google Scholar
• Cheng, Y. H., Jiang, B., Yang, M. K., & Gao, Z. (2010). Self-organizing fuzzy neural network-based actuator fault estimation for satellite attitude systems. Journal of Applied Sciences, 28(1), 72–76. https://www.jas.shu.edu.cn/EN/abstract/article_854.shtml
   Google Scholar
• De Persis, C., & Isidori, A. (2001). A geometric approach to nonlinear fault detection and isolation. IEEE Transactions on Automatic Control, 46(6), 853–865. https://doi.org/10.1109/9.928586
   Web of Science ®Google Scholar
• Dorf, R. C., & Bishop, R. H. (2008). Modern control systems. Pearson Prentice Hall.
   Google Scholar
• Freund, Y., & Schapire, R. E. (1999). Large margin classification using the perceptron algorithm. Machine Learning, 37(3), 277–296. https://doi.org/10.1023/A:1007662407062
   Web of Science ®Google Scholar
• Gao, C., Zhao, Q., & Duan, G. (2013). Robust actuator fault diagnosis scheme for satellite attitude control systems. Journal of the Franklin Institute, 350(9), 2560–2580. https://doi.org/10.1016/j.jfranklin.2013.02.021
   Web of Science ®Google Scholar
• Gou, J., Du, L., Zhang, Y., & Xiong, T. (2012). A new distance-weighted k-nearest neighbor classifier. Journal of Information and Computing Science, 9(6), 1429–1436.
   Google Scholar
• Henry, D. (2008). Fault diagnosis of microscope satellite thrusters using H∞/H− filters. Journal of Guidance, Control, and Dynamics, 31(3), 699–711. https://doi.org/10.2514/1.31003
   Web of Science ®Google Scholar
• Hou, Q., Cheng, Y., Lu, N., & Jiang, B. (2008, December). Study on FDD and FTC of satellite attitude control system based on the effectiveness factor. In 2008 2nd international symposium on systems and control in aerospace and astronautics (pp. 1–6). IEEE.
   Google Scholar
• Hu, D., Sarosh, A., & Dong, Y. F. (2012). A novel KFCM based fault diagnosis method for unknown faults in satellite reaction wheels. ISA Transactions, 51(2), 309–316. https://doi.org/10.1016/j.isatra.2011.10.005
   PubMed Web of Science ®Google Scholar
• Jiang, T., & Khorasani, K. (2007, October). A fault detection, isolation and reconstruction strategy for a satellite's attitude control subsystem with redundant reaction wheels. In 2007 IEEE international conference on systems, man and cybernetics (pp. 3146–3152). IEEE.
   Google Scholar
• Lin, S., & Ying, Z. (2012). Fault diagnosis of navigation satellite attitude control system based on data-driven combined with artificial intelligence. In China satellite navigation conference (CSNC) 2012 Proceedings (pp. 123–136). Springer.
   Google Scholar
• Mehra, R., Rago, C., & Seereeram, S. (1998, March). Autonomous failure detection, identification and fault-tolerant estimation with aerospace applications. In 1998 IEEE aerospace conference proceedings (Cat. No. 98TH8339) (Vol. 2, pp. 133–138). IEEE.
   Google Scholar
• Meng, Q., Zhang, T., Li, D. C., Liang, J. M., Liu, B., & Song, J. Y. (2013). Fault tolerant attitude control for flexible satellite with uncertainties and actuator saturation. International Journal of Advanced Robotic Systems, 10(6), 262. https://doi.org/10.5772/56362
   Google Scholar
• Meng, Q., Zhang, T., & Song, J. Y. (2013). Modified model-based fault-tolerant time-varying attitude tracking control of uncertain flexible satellites. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 227(11), 1827–1841. https://doi.org/10.1177/0954410012464011
   Web of Science ®Google Scholar
• Mitchell, T. M. (1997). Machine learning (No. 9, Vol. 1). McGraw-hill.
   Google Scholar
• Nagesh, I., & Edwards, C. (2011, September). A sliding mode observer based FDI scheme for a nonlinear satellite systems. In 2011 IEEE international conference on control applications (CCA) (pp. 159–164). IEEE.
   Google Scholar
• Nozari, H. A., Castaldi, P., Banadaki, H. D., & Simani, S. (2019). Novel non-model-based fault detection and isolation of satellite reaction wheels based on a mixed-learning fusion framework. IFAC-PapersOnLine, 52(12), 194–199. https://doi.org/10.1016/j.ifacol.2019.11.222
   Google Scholar
• Nozari, H. A., Nazeri, S., Banadaki, H. D., & Castaldi, P. (2018). Model-free fault detection and isolation of a benchmark process control system based on multiple classifiers techniques – A comparative study. Control Engineering Practice, 73, 134–148. https://doi.org/10.1016/j.conengprac.2018.01.007
   Web of Science ®Google Scholar
• Nozari, H. A., Shoorehdeli, M. A., Simani, S., & Banadaki, H. D. (2012). Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques. Neurocomputing, 91, 29–47. https://doi.org/10.1016/j.neucom.2012.02.014
   Web of Science ®Google Scholar
• Pandya, R., & Pandya, J. (2015). C5. 0 algorithm to improved decision tree with feature selection and reduced error pruning. International Journal of Computer Applications, 117(16), 18–21. https://doi.org/10.5120/20639-3318
   Google Scholar
• Patton, R. J., Uppal, F. J., Simani, S., & Polle, B. (2010). Robust FDI applied to thruster faults of a satellite system. Control Engineering Practice, 18(9), 1093–1109. https://doi.org/10.1016/j.conengprac.2009.04.011
   Web of Science ®Google Scholar
• Simani, S., & Fantuzzi, C. (2006). Dynamic system identification and model-based fault diagnosis of an industrial gas turbine prototype. Mechatronics, 16(6), 341–363. https://doi.org/10.1016/j.mechatronics.2006.01.002
   Web of Science ®Google Scholar
• Talebi, H. A., Patel, R. V., & Khorasani, K. (2007, October). Fault detection and isolation for uncertain nonlinear systems with application to a satellite reaction wheel actuator. In 2007 IEEE international conference on systems, man and cybernetics (pp. 3140–3145). IEEE.
   Google Scholar
• Tudoroiu, N., & Khorasani, K. (2007). Satellite fault diagnosis using a bank of interacting Kalman filters. IEEE Transactions on Aerospace and Electronic Systems, 43(4), 1334–1350. https://doi.org/10.1109/TAES.2007.4441743
   Web of Science ®Google Scholar
• Wie, B. (1998). Space vehicle dynamics and control. Aiaa.
   Google Scholar
• Wold, S., & Sjöström, M. (1977). SIMCA: A method for analyzing chemical data in terms of similarity and analogy.
   Google Scholar
• Wu, Q., & Saif, M. (2006, June). Robust fault diagnosis for a satellite large angle attitude system using an iterative neuron PID (INPID) observer. In 2006 American Control Conference (pp. 6-pp). IEEE.
   Google Scholar
• Wu, Q., & Saif, M. (2007, October). An overview of robust model-based fault diagnosis for satellite systems using sliding mode and learning approaches. In 2007 IEEE international conference on systems, man and cybernetics (pp. 3159–3164). IEEE.
   Google Scholar
• Zhao, S. L., & Zhang, Y. C. (2008, October). SVM classifier based fault diagnosis of the satellite attitude control system. In 2008 international conference on intelligent computation technology and automation (ICICTA) (Vol. 2, pp. 907–911). IEEE.
   Google Scholar
• Zuber, V., & Strimmer, K. (2009). Gene ranking and biomarker discovery under correlation. Bioinformatics (Oxford, England), 25(20), 2700–2707. https://doi.org/10.1093/bioinformatics/btp460
   PubMed Web of Science ®Google Scholar