Neural-network-based output feedback adaptive resilient control for stochastic nonlinear systems subject to sensor and actuator attacks

References

• An, L. W., & Yang, G. H. (2018). Improved adaptive resilient control against sensor and actuator attacks. Information Sciences, 423, 145–156. https://doi.org/10.1016/j.ins.2017.09.042
   Web of Science ®Google Scholar
• Chang, Y. H., Hu, Q., & Tomlin, C. J. (2018). Secure estimation based Kalman filter for cyber–physical systems against sensor attacks. Automatica, 95, 399–412. https://doi.org/10.1016/j.automatica.2018.06.010
   Web of Science ®Google Scholar
• Chen, W. S., Jiao, L. C., Li, J., & Li, R. (2010). Adaptive NN backstepping output-feedback control for stochastic nonlinear strict-feedback systems with time-varying delays. IEEE Transactions on Systems, Man, and Cybernetics: Cybernetics, 40(3), 939–950. https://doi.org/10.1109/TSMCB.2009.2033808
   PubMed Web of Science ®Google Scholar
• Chowdhury, N. R., Belikov, J., Baimel, D., & Levron, Y (2020). Observer-based detection and identification of sensor attacks in networked CPSs. Automatica, 121, 109166. https://doi.org/10.1016/j.automatica.2020.109166
   Web of Science ®Google Scholar
• Dehkordi, N. M., & Moussavi, S. Z. (2019). Distributed resilient adaptive control of islanded microgrids under sensor/actuator faults. IEEE Transactions on Smart Grid, 11(3), 2699–2708. https://doi.org/10.1109/TSG.5165411
   Web of Science ®Google Scholar
• Deng, H., & Kristic, M. (1997). Stochastic nonlinear stabilization—Part I: A backstepping design. Systems & Control Letters, 32(3),143–150. https://doi.org/10.1016/S0167-6911(97)00068-6
   Web of Science ®Google Scholar
• Deng, X. F., Zhang, C., & Ge, Y. (2022). Adaptive neural network dynamic surface control of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault. Journal of Franklin Institute, 359(9), 4054–4073. https://doi.org/10.1016/j.jfranklin.2022.04.010
   Web of Science ®Google Scholar
• Ding, D. R., Wang, Z. D., Han, Q. L., & Wei, G. L. (2018). Security control for discrete-time stochastic nonlinear systems subject to deception attacks. IEEE Transactions Systems, Man, and Cybernetics: Systems, 48(5), 779–789. https://doi.org/10.1109/TSMC.2016.2616544
   Web of Science ®Google Scholar
• Ding, K., Ren, X., Quevedo, D. E., Dey, S., & Shi, L. (2019). DoS attacks on remote state estimation with asymmetric information. IEEE Transactions on Control of Network Systems, 6(2), 653–666. https://doi.org/10.1109/TCNS.6509490
   Web of Science ®Google Scholar
• Guo, Q., Zhang, Y., Celler, B. G., & Steven Su, W. (2016). Backstepping control of electro-hydraulic system based on extended-state-observer with plant dynamics largely unknown. IEEE Transactions on Industrial Electronics, 63(11), 6909–6920. https://doi.org/10.1109/TIE.2016.2585080
   Web of Science ®Google Scholar
• Guo, Z., Shi, D., Johansson, K. H., & Shi, L. (2018). Worst-case stealthy innovation-based linear attack on remote state estimation. Automatica, 89, 117–124. https://doi.org/10.1016/j.automatica.2017.11.018
   Web of Science ®Google Scholar
• He, H. F., Qi, W. H., Liu, Z. T., & Wang, M. L. (2021). Adaptive attack-resilient control for Markov jump system with additive attacks. Nonlinear Dynamics, 103(2), 1585–1598. https://doi.org/10.1007/s11071-020-06085-5
   Web of Science ®Google Scholar
• Hu, L., Wang, Z., Han, Q. L., & Liu, X. (2018). State estimation under false data injection attacks: Security analysis and system protection. Automatica, 87, 176–183. https://doi.org/10.1016/j.automatica.2017.09.028
   Web of Science ®Google Scholar
• Jin, X., & Haddad, W. M. (2019). An adaptive control architecture for leader–follower multiagent systems with stochastic disturbances and sensor and actuator attacks. International Journal of Control, 92(11), 2561–2570. https://doi.org/10.1080/00207179.2018.1450524
   Web of Science ®Google Scholar
• Jin, X., Haddad, W. M., & Yucelen, T. (2017). An adaptive control architecture for mitigating sensor and actuator attacks in cyber–physical systems. IEEE Transactions on Automatic Control, 62(11), 6058–6064. https://doi.org/10.1109/TAC.2017.2652127
   Web of Science ®Google Scholar
• Jin, X., & Li, Y. X. (2021). Adaptive fuzzy control of uncertain stochastic nonlinear systems with full state constraints. Information Sciences, 574, 625–639. https://doi.org/10.1016/j.ins.2021.07.056
   Web of Science ®Google Scholar
• Li, T. S., Li, Z. F., Wang, D., & Philp Chen, C. L. (2015). Output-feedback adaptive neural control for stochastic nonlinear time-varying delay systems with unknown control directions. IEEE Transactions on Neural Networks and Learning Systems, 26(6), 1188–1201. https://doi.org/10.1109/TNNLS.2014.2334638
   PubMed Web of Science ®Google Scholar
• Li, Y., Shi, L., & Chen, T. (2018). Detection against linear deception attacks on multi-sensor remote state estimation. IEEE Transactions on Control of Network Systems, 5(3), 846–856. https://doi.org/10.1109/TCNS.2017.2648508
   Web of Science ®Google Scholar
• Li, Y. M., Tong, S. C., & Li, T. S. (2015). Observer-based adaptive fuzzy tracking control of MIMO stochastic nonlinear systems with unknown control directions and unknown dead-zones. IEEE Transactions on Fuzzy Systems, 23(4), 1228–1241. https://doi.org/10.1109/TFUZZ.2014.2348017
   Web of Science ®Google Scholar
• Lin, W., & Gong, Q. (2003). A remark on partial-state feedback stabilization of cascade systems using small gain theorem. IEEE Transactions on Automatic Control, 48(3), 497–500. https://doi.org/10.1109/TAC.2003.809166
   Web of Science ®Google Scholar
• Liu, C., & Zhai, J. Y (2022). Adaptive output feedback controller design for high-order stochastic nonlinear systems with uncertain output function. ISA Transactions, 130, 195–204. https://doi.org/10.1016/j.isatra.2022.04.024
   PubMed Web of Science ®Google Scholar
• Liu, Y., Zhang, H. G., Wang, Y. C., Ren, H., & Li, Q. C. (2022). Adaptive fuzzy prescribed finite-time tracking control for nonlinear system with unknown control directions. IEEE Transactions on Fuzzy Systems, 30(6), 1993–2003. https://doi.org/10.1109/TFUZZ.2021.3072696
   Web of Science ®Google Scholar
• Liu, Y. C., & Zhu, Q. D. (2021). Adaptive neural network asymptotic tracking control for nonstrict feedback stochastic nonlinear systems. Neural Networks, 143, 283–290. https://doi.org/10.1016/j.neunet.2021.06.011
   PubMed Web of Science ®Google Scholar
• Lv, W. S. (2021). Finite-time adaptive neural control for nonlinear systems under state-dependent sensor attacks. International Journal of Robust and Nonlinear Control, 31(10), 4689–4704. https://doi.org/10.1002/rnc.v31.10
   Web of Science ®Google Scholar
• Mishra, S., Shoukry, Y., Karamchandani, N., Diggavi, S. N., & Tabuada, P. (2017). Secure state estimation against sensor attacks in the presence of noise. IEEE Transactions on Control of Network System, 4(1), 49–59. https://doi.org/10.1109/TCNS.2016.2606880
   Web of Science ®Google Scholar
• Pasqualetti, F., Dorfler, F., & Bullo, F. (2013). Attack detection and identification in cyber–physical systems. IEEE Transactions on Automatic Control, 58(11), 2715–2729. https://doi.org/10.1109/TAC.2013.2266831
   Web of Science ®Google Scholar
• Polycarpou, M. M. (1996). Stable adaptive neural control scheme for nonlinear systems. IEEE Transactions on Automatic Control, 41(3), 447–451. https://doi.org/10.1109/9.486648
   Web of Science ®Google Scholar
• Psillakis, H. E., & Alexandridis, A. T. (2007). NN-based adaptive tracking control of uncertain nonlinear systems disturbed by unknown covariance noise. IEEE Transactions on Neural Networks, 18(6), 1830–1835. https://doi.org/10.1109/TNN.2007.901274
   Web of Science ®Google Scholar
• Ren, X. X., & Yang, G. H. (2020). Adaptive control for nonlinear cyber–physical systems under false data injection attacks through sensor networks. International Journal Robust and Nonlinear Control, 30(1), 65–79. https://doi.org/10.1002/rnc.v30.1
   Web of Science ®Google Scholar
• Shi, D., Guo, Z., Johansson, K. H., & Shi, L. (2018). Causality countermeasures for anomaly detection in cyber–physical systems. IEEE Transactions on Automatic Control, 63(2), 386–401. https://doi.org/10.1109/TAC.9
   Web of Science ®Google Scholar
• Shoukry, Y., Nuzzo, P., Puggelli, A., Sangiovanni-Vincentelli, A. L., Seshia, S. A., & Tabuada, P. (2017). Secure state estimation for cyber–physical systems under sensor attacks: A satisfiability modulo theory approach. IEEE Transactions on Automatic Control, 62(10), 4917–4932. https://doi.org/10.1109/TAC.2017.2676679
   Web of Science ®Google Scholar
• Su, Q. Y., Fan, Z. X., Long, Y., & Li, J. (2020). Attack detection and secure state estimation for cyber–physical systems with finite-frequency observers. Journal of Franklin Institute, 357(17), 12724–12741. https://doi.org/10.1016/j.jfranklin.2020.08.041
   Web of Science ®Google Scholar
• Sung, J. Y (2020). Neural-network-based adaptive resilient dynamic surface control against unknown deception attacks of uncertain nonlinear time-delay cyber physical systems. IEEE Transactions on Neural Networks and Learning Systems, 31(10), 4341–4353. https://doi.org/10.1109/TNNLS.5962385
   PubMed Web of Science ®Google Scholar
• Tong, S. C., Wang, T., & Li, Y. M. (2014). Fuzzy adaptive actuator failure compensation control of uncertain stochastic nonlinear systems with unmodeled dynamics. IEEE Transactions on Fuzzy Systems, 22(3), 563–574. https://doi.org/10.1109/TFUZZ.91
   Web of Science ®Google Scholar
• Tong, S. C., Li, Y. M., & Sui, S. (2016). Adaptive fuzzy output feedback control for switched nonstrict-feedback nonlinear systems with input nonlinearities. IEEE Transactions on Fuzzy Systems, 24(6), 1426–1440. https://doi.org/10.1109/TFUZZ.2016.2516587
   Web of Science ®Google Scholar
• Vamvoudakis, K. G., Hespanha, J. P., Sinopoli, B., & Mo, Y. (2014). Detection in adversarial environments. IEEE Transactions on Automatic Control, 59(12), 3209–3223. https://doi.org/10.1109/TAC.2014.2351671
   Web of Science ®Google Scholar
• Wang, H. Q., Liu, K. F., Liu, X. P., Chen, B., & Lin, C. (2015). Neural-based adaptive output-feedback control for a class of nonstrict-feedback stochastic nonlinear systems. IEEE Transactions on Cybernetics, 45(9), 1977–1987. https://doi.org/10.1109/TCYB.2014.2363073
   PubMed Web of Science ®Google Scholar
• Wang, F., Liu, Z., Zhang, Y., & Philip Chen, C. L. (2016). Adaptive fuzzy control for a class of stochastic pure-feedback nonlinear systems with unknown hysteresis. IEEE Transactions on Fuzzy Systems, 24(1), 140–152. https://doi.org/10.1109/TFUZZ.2015.2446531
   Web of Science ®Google Scholar
• Wei, A., Song, Y. D., Wen, C. Y., & Lai, J. F. (2018). Finite time attack detection and supervised secure state estimation for CPSs with malicious adversaries. Information Sciences, 451/452, 67–82. https://doi.org/10.1016/j.ins.2018.03.056
   Web of Science ®Google Scholar
• Wu, J., Su, Y. B., & Zhao, Q. J. (2022). Adaptive asymptotic control of stochastic systems with state delay and unknown control directions. IEEE Transactions on Circuits Systems II: Express Briefs, 69(1), 174–178. https://doi.org/10.1109/TCSII.2021.3075884
   Web of Science ®Google Scholar
• Xia, J. W., Lian, Y. X., Su, S. F., Shen, H., & Chen, G. L. (2022). Observer-based event-triggered adaptive fuzzy control for unmeasured stochastic nonlinear systems with unknown control directions. IEEE Transactions on Cybernetics, 52(10), 10655–10666. https://doi.org/10.1109/TCYB.2021.3069853
   PubMed Web of Science ®Google Scholar
• Yang, Y. K., Yu, Z. X., & Li, S. G. (2018). Adaptive output feedback quantised tracking control for stochastic nonstrict-feedback nonlinear systems with input saturation. International Journal of System Sciences, 49(15), 3130–3145. https://doi.org/10.1080/00207721.2018.1534025
   Web of Science ®Google Scholar
• Ye, X. D., & Jiang, J. P. (1998). Adaptive nonlinear design without a prior knowledge of control directions. IEEE Transactions on Automatic Control, 43(11), 1617–1621. https://doi.org/10.1109/9.728882
   Web of Science ®Google Scholar
• Yu, Z. X., & Du, H. B. (2011). Adaptive neural control for uncertain stochastic nonlinear strict-feedback systems with time-varying delays: A Razumikhin functional method. Neurocomputing, 74(12–13), 2072–2082. https://doi.org/10.1016/j.neucom.2010.12.030
   Web of Science ®Google Scholar
• Yu, Z. X., Li, S. G., & Du, H. B. (2013). Razumikhin-Nussbaum-lemma-based adaptive neural control for uncertain stochastic pure-feedback nonlinear systems with time-varying delays. International Journal of Robust and Nonlinear Control, 23(11), 1214–1239. https://doi.org/10.1002/rnc.v23.11
   Web of Science ®Google Scholar
• Yu, Z. X., & Li, S. G. (2014). Neural-network-based output-feedback adaptive dynamic surface control for a class of stochastic nonlinear time-delay systems with unknown control directions. Neurocomputing, 129, 540–547. https://doi.org/10.1016/j.neucom.2013.09.005
   Web of Science ®Google Scholar
• Yu, Z. X., Li, S. G., Yu, Z. S., & Li, F. F. (2018). Adaptive neural output feedback control for non-strict-feedback stochastic nonlinear systems with unknown backlash-like hysteresis and unknown control directions. IEEE Transactions on Neural Networks and Learning Systems, 29(4), 1147–1160. https://doi.org/10.1109/TNNLS.2017.2669088
   PubMed Web of Science ®Google Scholar
• Yucelen, T., Haddad, W. M., & Feron, E. M. (2016). Adaptive control architectures for mitigating sensor attacks in cyber–physical systems. Cyber–Physical Systems, 2(1–4), 24–52. https://doi.org/10.1080/23335777.2016.1244562
   Google Scholar
• Yue, Y., Huang, J. S., Su, X. J., Wang, K., & Li, G. Q. (2022). Adaptive control of second-order nonlinear systems with injection and deception attacks. IEEE Transactions on Systems, Man, and Cybernetics: Systems., 52(1), 574–581. https://doi.org/10.1109/TSMC.2020.3003801
   Web of Science ®Google Scholar
• Zhang, T. P., & Xia, X. N (2015). Adaptive output feedback tracking control of stochastic nonlinear systems with dynamic uncertainties. International Journal of Robust and Nonlinear Control, 25(9), 1282–1300. https://doi.org/10.1002/rnc.v25.9
   Web of Science ®Google Scholar