Open Access
Issue
Security and Safety
Volume 1, 2022
Article Number 2022004
Number of page(s) 29
Section Industrial Control
DOI https://doi.org/10.1051/sands/2022004
Published online 08 August 2022
  1. Frank PM. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - A survey. Automatica 1990; 26: 459–74. [CrossRef] [Google Scholar]
  2. Frank PM and Ding X. Survey of robust residual generation and evaluation methods in observer-based fault detection systems. J Process Contr 1997; 7: 403–24. [CrossRef] [Google Scholar]
  3. Ding SX, Zhang P and Yin S et al. An integrated design framework of fault-tolerant wireless networked control systems for industrial automatic control applications. IEEE Trans Ind Inform 2013; 9: 462–71. [CrossRef] [Google Scholar]
  4. Gao ZW, Cecati C and Ding SX. A survey of fault diagnosis and fault-tolerant techniques, part I: Fault diagnosis with model-based and signal-based approaches. IEEE Trans Ind Electron 2015; 62: 3757–67. [CrossRef] [Google Scholar]
  5. Hwang I, Kim S and Kim Y et al. A survey of fault detection, isolation, and reconfiguration methods. IEEE Trans Contr Syst Tech 2010; 18: 636–53. [CrossRef] [Google Scholar]
  6. Wen CL, Lv FY and Bao ZJ et al. A review of data driven-based incipient fault diagnosis. Acta Automat Sin 2016; 42: 1285–99. [Google Scholar]
  7. Zhou DH, Zhao Y and Wang Z et al. Review on diagnosis techniques for intermittent faults in dynamic systems. IEEE Trans Ind Electron 2020; 67: 2337–47. [CrossRef] [Google Scholar]
  8. Dibaji SM, Pirani M and Flamholz DB et al. A systems and control perspective of CPS security. Ann Rev Contr 2019; 47: 394–411. [CrossRef] [Google Scholar]
  9. Ding D, Han QL and Xiang Y et al. A survey on security control and attack detection for industrial cyber-physical systems. Neurocomputing 2018; 275: 1674–83. [CrossRef] [Google Scholar]
  10. Giraldo J, Urbina D and Cardenas A et al. A survey of physics-based attack detection in cyber-physical systems. ACM Comput Surv 2018; 51: 76. [Google Scholar]
  11. Pasqualetti F, Doerfler F and Bullo F. Attack detection and identification in cyber-physical systems. IEEE Trans Automat Contr 2013; 58: 2715–29. [CrossRef] [Google Scholar]
  12. Tan S, Guerrero JM and Xie P et al. Brief survey on attack detection methods for cyber-physical systems. IEEE Syst J 2020; 14: 5329–39. [CrossRef] [Google Scholar]
  13. Yan W, Mestha LK and Abbaszadeh M. Attack detection for securing cyber physical systems. IEEE Internet Things J 2019; 6: 8471–81. [CrossRef] [Google Scholar]
  14. Zhang D, Wang Q-G and Feng G et al. A survey on attack detection, estimation and control of industrial cyber-physical systems. ISA Trans 2021; 116: 1–16. [CrossRef] [PubMed] [Google Scholar]
  15. Zhou C, Hu B and Shi Y et al. A unified architectural approach for cyberattack-resilient industrial control systems. Proc IEEE 2021; 109: 517–41. [CrossRef] [Google Scholar]
  16. Ding SX. Advanced Methods for Fault Diagnosis and Fault-tolerant Control. Berlin: Springer-Verlag, 2020. [Google Scholar]
  17. Griffioen P, Weerakkody S and Sinopoli B. A moving target defense for securing cyber-physical systems. IEEE Trans Automat Contr 2021; 66: 2016–31. [CrossRef] [Google Scholar]
  18. Schellenberger C and Zhang P. Detection of covert attacks on cyber-physical systems by extending the system dynamics with an auxiliary system. In: 2017 IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, Australia, 2017, 1374–9. [CrossRef] [Google Scholar]
  19. Weerakkody S and Sinopoli B. Detecting integrity attacks on control systems using a moving target approach. In: 2015 54th IEEE Conference on Decision and Control (CDC), Osaka, Japan, 2015, 5820–6. [CrossRef] [Google Scholar]
  20. Ferrari RMG and Teixeira AMH. A switching multiplicative watermarking scheme for detection of stealthy cyberattacks. IEEE Trans Automat Contr 2021; 66: 2558–73. [CrossRef] [Google Scholar]
  21. Mo Y, Weerakkody S and Sinopoli B. Physical authentication of control systems: Designing watermarked control inputs to detect counterfeit sensor outputs. IEEE Contr Syst Mag 2015; 35: 93–109. [Google Scholar]
  22. Porter M, Hespanhol P and Aswani A et al. Detecting generalized replay attacks via time-varying dynamic watermarking. IEEE Trans Automat Contr 2021; 66: 3502–17. [CrossRef] [Google Scholar]
  23. Ding SX, Li L and Zhao D et al. Application of the unified control and detection framework to detecting stealthy integrity cyberattacks on feedback control systems. Automatica 2022; 142: 110352. [CrossRef] [Google Scholar]
  24. Ding SX, Li L and Liu T. An alternative paradigm of fault diagnosis in dynamic systems: Orthogonal projection-based methods. ArXiv preprint [arXiv:2202.08108], 2022. [Google Scholar]
  25. Ding SX and Li L. Control performance monitoring and degradation recovery in automatic control systems: A review, some new results, and future perspectives. Contr Eng Pract 2021; 111: 104790. [CrossRef] [Google Scholar]
  26. Vinnicombe G. Uncertainty and Feedback: H∞ Loop-Shaping and the ν Gap Metric. London, UK: World Scientific, 2000. [CrossRef] [Google Scholar]
  27. Zhou K. Essential of Robust Control. Englewood Cliffs, NJ: Prentice-Hall, 1998. [Google Scholar]
  28. Ding SX, Yang G and Zhang P et al. Feedback control structures, embedded residual signals and feedcak control schemes with an integrated residual access. IEEE Trans Contr Syst Tech 2010; 18: 352–67. [CrossRef] [Google Scholar]
  29. Li L, Luo H and Ding SX et al. Performance-based fault detection and fault-tolerant control for automatic control systems. Automatica 2019; 99: 309–16. [Google Scholar]
  30. Schulze DM, Alexandru AB and Quevedo DE et al. Encrypted control for networked systems: An illustrative introduction and current challenges. IEEE Contr Syst Mag 2021; 41: 58–78. [CrossRef] [Google Scholar]
  31. Feintuch A. Robust Control Theory in Hilbert Space. New York: Springer-Verlag, 1998. [CrossRef] [Google Scholar]
  32. Han H, Yang Y and Li L et al. Control performance-based fault detection and fault-tolerant control schemes for a class of nonlinear systems. Int J Robust Nonlinear Control 2019; 30: 1431–50. [Google Scholar]
  33. Han H, Yang Y and Li L et al. Performance-based fault detection and fault-tolerant control for nonlinear systems with t-s fuzzy implementation. IEEE Trans Cybern 2021; 51: 801–14. [CrossRef] [PubMed] [Google Scholar]
  34. Ding SX. Model-Based Fault Diagnosis Techniques - Design Schemes, Algorithms, and Tools. Berlin: Springer-Verlag, 2008. [Google Scholar]
  35. Francis BA. A Course in H-Infinity Control Theory. Berlin - New York: Springer-Verlag, 1987. [CrossRef] [Google Scholar]
  36. Kato T. Perturbation Theory for Linear Operators. Berlin: Springer-Verlag, 1995. [CrossRef] [Google Scholar]
  37. Hoffmann JW. Normalized coprime factorizations in continuous and discrete time - a joint state-space approach. IMA J Math Contr Inform 1996; 13: 359–84. [CrossRef] [Google Scholar]
  38. Li L and Ding SX. Gap metric techniques and their application to fault detection performance analysis and fault isolation schemes. Automatica 2020; 118: 109029. [CrossRef] [Google Scholar]
  39. Van der Schaft A. L2 - Gain and Passivity Techniques in Nonlinear Control. London: Springer, 2000. [CrossRef] [Google Scholar]
  40. Bengio Y, Courville A and Vincent P. Representation learning: A review and new perspectives. IEEE Trans Pattern Anal Mach Intell 2013; 35: 1798–1828. [CrossRef] [PubMed] [Google Scholar]
  41. Geiger BC. On information plane analyses of neural network classifiers-a review. IEEE Trans Neural Netw Learn Syst 2021, in press. https://doi.org/10.1109/TNNLS.2021.3089037. [Google Scholar]
  42. Burkart N and Huber MF. A survey on the explainability of supervised machine learning. J Artif Intell Res 2021; 70: 245–317. [CrossRef] [Google Scholar]
  43. Bauer M, Horch A and Xie L et al. The current state of control loop performance monitoring, a survey of application in industry. J Process Contr 2016; 38: 1–10. [CrossRef] [Google Scholar]
  44. Li L and Ding SX. Performance supervised fault detection schemes for industrial feedback control systems and their data-driven implementation. IEEE Trans Ind Inform 2020; 16: 2849–58. [CrossRef] [Google Scholar]
  45. Perez T, Goodwin GC and Seron MM. Performance degradation in feedback control due to constraints. IEEE Trans Automat Contr 2003; 48: 1381–85. [CrossRef] [Google Scholar]
  46. Zhang Y and Jiang J. Fault tolerant control system design with explicit consideration of performance degradation. IEEE Trans Aerosp Electron Syst 2003; 39: 838–48. [CrossRef] [Google Scholar]
  47. Zhang Y, Jiang J and Theilliol D. Incorporating performance degradation in fault tolerant control system design with multiple actuator failures. J Contr Automat Syst 2008; 6: 327–38. [Google Scholar]
  48. Li L, Ding SX and Luo H et al. Performance-based fault-tolerant control approaches for industrial processes with multiplicative faults. IEEE Trans Ind Inform 2020; 16: 4759–68. [CrossRef] [Google Scholar]
  49. Li L, Li S and Ding SX et al. Riemannian metric based performance monitoring and diagnosis for a class of feedback control systems. Acta Automat Sin 2022, in press. https://doi.org/10.16383/j.aas.c210027. [Google Scholar]
  50. Magnus JR. Linear Structures. Oxford, UK: Oxford University Press, 1988. [Google Scholar]
  51. Parr R, Li L and Taylor G et al. An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning. In: Proceedings of the 25th International Conference on Machine Learning. ICML '08, 2008. Association for Computing Machinery, New York, NY, USA, 752–9. [Google Scholar]
  52. Al-Tamimi A, Lewis FL and Abu-Khalaf M. Discrete-time nonlinear hjb solution using approximate dynamic programming: Convergence proof. IEEE Trans Syst Man Cybern Part B (Cybern) 2008; 38: 943–9. [CrossRef] [PubMed] [Google Scholar]
  53. Shang C, Ding SX and Ye H. Distributionally robust fault detection design and assessment for dynamical systems. Automatica 2021; 125: 109434. [CrossRef] [Google Scholar]
  54. Wan Y, Ma Y and Zhong M. Distributionally robust trade-off design of parity relation based fault detection systems. Int J Robust Nonlinear Contr 2021; 31: 9149–74. [CrossRef] [Google Scholar]
  55. Xue T, Zhong M and Li L et al. An optimal data-driven approach to distribution independent fault detection. IEEE Trans Ind Inform 2020; 16: 6826–36. [CrossRef] [Google Scholar]
  56. Lin F, Fang X and Gao Z. Distributionally robust optimization: A review on theory and applications. Numer Algeb Contr Optim 2022; 12: 159–212. [CrossRef] [Google Scholar]
  57. Rahimian H and Mehrotra S. Distributionally robust optimization: A review. ArXiv preprint [arXiv:1908.05659], 2019. [Google Scholar]
  58. Yang I. A dynamic game approach to distributionally robust safety specifications for stochastic systems. Automatica 2018; 94: 94–101. [CrossRef] [Google Scholar]
  59. Xu Y, Ding SX and Yin S et al. Performance degradation monitoring and recovery of vision-based control systems. IEEE Trans Contr Syst Technol 2021; 29: 2712–9. [CrossRef] [Google Scholar]
  60. Lei Y, Li N and Guo L et al. Machinery health prognostics: A systematic review from data acquisition to rul prediction. Mech Syst Signal Process 2018; 104: 799–834. [CrossRef] [Google Scholar]
  61. Liao L and Köttig F. Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Trans Reliabil 2014; 63: 191–207. [CrossRef] [Google Scholar]
  62. Si X, Ren Z and Hu X et al. A novel degradation modeling and prognostic framework for closed-loop systems with degrading actuator. IEEE Trans Ind Electron 2020; 67: 9635–47. [CrossRef] [Google Scholar]
  63. Yin S, Xiao B and Ding SX et al. A review on recent development of spacecraft attitude fault-tolerant control system. IEEE Trans Ind Electron 2016; 63: 3311–20. [CrossRef] [Google Scholar]

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