In this paper, the design of fault detection filter (FDF) for a class of unmanned surface vehicle (USV) systems based on dynamic probabilistic event-triggered transmission protocol under compound cyber attacks is studied. First, the dynamic probabilistic event-triggered mechanism and numerical quantization are used to boost system performance, reduce communication resource consumption, and prevent network congestion. Considering Denial-of-service (DoS) attack and spoofing attack, a compound attack model is established, which results in the filtering error system being converted into a stochastic switching system. And the sufficient conditions of the mean-square exponential stability for the USV system are obtained by using the Lyapunov functional and linear matrix inequality (LMI) technique. The gains of the designed filter and weighting matrix are obtained by optimizing the LMIs. Finally, a practical example is given to verify the feasibility of the proposed fault detection method.
AbidAKhanMTIqbalJ (2021) A review on fault detection and diagnosis techniques: Basics and beyond. Artificial Intelligence Review54(5): 3639–3664.
2.
ChenCLPYuDLiuL (2018) Automatic leader-follower persistent formation control for autonomous surface vehicles. IEEE Access7: 12146–12155.
3.
ChenHGaoFZongG (2021) Finite-time dissipative fuzzy state estimation for jump systems with mixed cyber attacks: A probabilistic event-triggered approach. IEEE Transactions on Cybernetics53(6): 3493–3505.
4.
ChenHZongGGaoF, et al. (2023) Probabilistic event-triggered policy for extended dissipative finite-time control of MJSs under cyber-attacks and actuator failures. IEEE Transactions on Automatic Control68(12): 7803–7810.
5.
ChengJXieLZhangD, et al. (2023) Novel event-triggered protocol to sliding mode control for singular semi-Markov jump systems. Automatica151: 110906.
6.
DuJHuXLiuH, et al. (2015) Adaptive robust output feedback control for a marine dynamic positioning system based on a high-gain observer. IEEE Transactions on Neural Networks and Learning Systems26(11): 2775–2786.
7.
HaoLYYuYLiH (2019) Fault tolerant control of UMV based on sliding mode output feedback. Applied Mathematics and Computation359: 433–455.
8.
LiHParkJH (2024) Adaptive event-triggered fault detection filter for unmanned surface vehicles against randomly occurring injection attacks. Automatica163: 111555.
9.
LiXLiuZQiuZ, et al. (2020) Event-triggered h-infinity synchronization control for switched master-slave systems subject to stochastic cyber attacks and quantization. Optimal Control Applications and Methods41(2): 690–708.
10.
LiangXQuXHouY, et al. (2020) Distributed coordinated tracking control of multiple unmanned surface vehicles under complex marine environments. Ocean Engineering205: 107328.
11.
LiuCLeeSVarnhagenS, et al. (2017) Path planning for autonomous vehicles using model predictive control. In: 2017 IEEE intelligent vehicles symposium (IV), Los Angeles, CA, 11–14 June, pp. 174–179. New York: IEEE.
12.
LiuJWangYCaoJ, et al. (2020) Secure adaptive-event-triggered filter design with input constraint and hybrid cyber attack. IEEE Transactions on Cybernetics51(8): 4000–4010.
13.
LiuJWangYZhaL, et al. (2021) An event-triggered approach to security control for networked systems using hybrid attack model. International Journal of Robust and Nonlinear Control31(12): 5796–5812.
14.
LiuJYinTXieX, et al. (2019) Event-triggered state estimation for t–s fuzzy neural networks with stochastic cyber-attacks. International Journal of Fuzzy Systems21(2): 532–544.
15.
LiuQLongYLiT, et al. (2022) Fault detection for unmanned marine vehicles under replay attack. IEEE Transactions on Fuzzy Systems31(5): 1716–1728.
16.
LiuZZhangYYuX, et al. (2016) Unmanned surface vehicles: An overview of developments and challenges. Annual Reviews in Control41: 71–93.
17.
MaYNieZHuS, et al. (2020) Fault detection filter and controller co-design for unmanned surface vehicles under DoS attacks. IEEE Transactions on Intelligent Transportation Systems22(3): 1422–1434.
18.
QiWYinHParkJH, et al. (2024) Intelligent finite-time protocol-based stabilization for networked umv systems with DoS attacks. IEEE Transactions on Intelligent Transportation Systems25: 16734–16744.
19.
ShiDElliottRJChenT (2016) On finite-state stochastic modeling and secure estimation of cyber-physical systems. IEEE Transactions on Automatic Control62(1): 65–80.
20.
WangJZhuangGXiaJ, et al. (2022) Asynchronous dissipative control and robust exponential mean square stabilization for uncertain fuzzy neutral Markov jump systems. Journal of Systems Science and Complexity35(4): 1374–1397.
21.
WangQPengLZhaoH, et al. (2023) Adaptive event-triggered filtering for semi-Markov jump systems under communication constraints. Applied Mathematics and Computation459: 128275.
22.
WangXFeiZGaoH, et al. (2019) Integral-based event-triggered fault detection filter design for unmanned surface vehicles. IEEE Transactions on Industrial Informatics15(10): 5626–5636.
23.
WangYLHanQL (2016) Network-based fault detection filter and controller coordinated design for unmanned surface vehicles in network environments. IEEE Transactions on Industrial Informatics12(5): 1753–1765.
24.
WooJKimN (2020) Collision avoidance for an unmanned surface vehicle using deep reinforcement learning. Ocean Engineering199: 107001.
25.
XingSLiMDengF (2025) Dynamic event-triggered consensus tracking control for nonlinear stochastic multi-agent systems under dual network attacks. International Journal of Robust and Nonlinear Control35(2): 706–716.
26.
YanSYangXGuZ, et al. (2024) Dynamic sum-based event-triggered h-infinity filtering for networked TS fuzzy wind turbine systems with deception attacks. Fuzzy Sets and Systems493: 109084.
27.
YeZZhangDChengJ, et al. (2022) Event-triggering and quantized sliding mode control of UMV systems under DoS attack. IEEE Transactions on Vehicular Technology71(8): 8199–8211.
28.
YeZZhangDWuZG, et al. (2021) A3c-based intelligent event-triggering control of networked nonlinear unmanned marine vehicles subject to hybrid attacks. IEEE Transactions on Intelligent Transportation Systems23(8): 12921–12934.
29.
ZhangDYeZFengG, et al. (2021) Intelligent event-based fuzzy dynamic positioning control of nonlinear unmanned marine vehicles under DoS attack. IEEE Transactions on Cybernetics52(12): 13486–13499.
30.
ZhouCGuSWenY, et al. (2020) The review unmanned surface vehicle path planning: Based on multi-modality constraint. Ocean Engineering200: 107043.
