Distributed finite‐time adaptive fault‐tolerant consensus control of second‐order multi‐agent systems under deception attacks
Pages 1309 - 1326
Summary
This study addresses the issue of distributed fault‐tolerant consensus control for second‐order multi‐agent systems subject to simultaneous actuator bias faults in the physical layer and deception attacks in the cyber layer. Cyber‐physical threats (malicious state‐coupled nonlinear attacks and physical deflection faults), unknown control gains, external disturbances and uncertainties force the failure of the existing graph theory‐based consensus control schemes, leading to disruptions in the cooperation and coordination of multi‐agent systems. Then, the power integrator‐based virtual control is incorporated in the distributed fault‐tolerant consensus to achieve unknown parameter estimations with the adaptive technique. The consensus‐based robustness to lumped uncertainties, resilience to attacks, compensation to faults, and novel finite‐time convergence of the neighborhood errors and velocity errors are also realized within a prescribed finite‐time settling bound. The simulation is conducted to verify the effectiveness of the distributed finite‐time adaptive fault‐tolerant consensus algorithm.
References
[1]
Kada B, Khalid M, Shaikh M. Distributed cooperative control of autonomous multi‐agent UAV systems using smooth control. J Syst Eng Electron. 2020;31(6):1297‐1307.
[2]
Zong C, Ji Z, Tian L, Zhang Y. Distributed multi‐robot formation control based on bipartite consensus with time‐varying delays. IEEE Access. 2019;7:144790‐144798.
[3]
Liang S, Wang F, Chen Z, Liu Z. Formation control for discrete‐time heterogeneous multi‐agent systems. Int J Robust Nonlinear Control. 2022;32(10):5848‐5865.
[4]
Amirkhani A, Barshooi A. Consensus in multi‐agent systems: a review. Artif Intell Rev. 2022;55(5):3897‐3935.
[5]
Zheng K, Fan H, Liu L, Cheng Z. Triggered finite‐time consensus of first‐order multi‐agent systems with input saturation. IET Control Theory Appl. 2022;16(4):464‐474.
[6]
Zhu J, Wen G, Li B. Decentralized adaptive formation control based on sliding mode strategy for a class of second‐order nonlinear unknown dynamic multi‐agent systems. Int J Adapt Control Signal Process. 2022;36(4):1045‐1058.
[7]
Sun J, Wang Z. Event‐triggered consensus control of high‐order multi‐agent systems with arbitrary switching topologies via model partitioning approach. Neurocomputing. 2020;413:14‐22.
[8]
Yu L, Liu Y. Asymptotic consensus via switching adaptive for nonlinear multi‐agent systems subject to atypical disturbances. Int J Adapt Control Signal Process. 2021;35:877‐891.
[9]
Wang G, Wang C, Ding Z, Ji Y. Distributed consensus of nonlinear multi‐agent systems with mismatched uncertainties and unknown high‐frequency gains. IEEE Trans Circuits Syst II Express Briefs. 2021;68(3):938‐942.
[10]
Ali MS, Tajudeen MM, Rajchakit G, Priya B, Thakur GK. Adaptive event‐triggered pinning synchronization control for complex networks with random saturation subject to hybrid cyber‐attacks. Int J Adapt Control Signal Process. 2023;37:2041‐2062.
[11]
He W, Xu W, Ge X, Han Q, Du W, Qian F. Secure control of multiagent systems against malicious attacks: a brief survey. IEEE Trans Industr Inform. 2022;18(6):3595‐3608.
[12]
Zhang Z, Wang C, Cai X. Consensus control of higher‐order nonlinear multi‐agent systems with unknown control directions. Neurocomputing. 2019;359:122‐129.
[13]
Gu X, Zhou W, Wu Y, Wu W, Yang G. Distributed bipartite consensus control for multi‐agent systems with actuator faults under undirected topology. In: Jia Y, Zhang W, Fu Y, Yu Z, Zheng S, eds. Proceedings of 2021 Chinese Intelligent Systems Conference: Volume II. Springer; 2022:359‐367.
[14]
Khalili M, Zhang X, Polycarpou M, Parisini T, Cao Y. Distributed adaptive fault‐tolerant control of uncertain multi‐agent systems. Automatica. 2018;87:142‐151.
[15]
Zhao L, Yang G. Cooperative adaptive fault‐tolerant control for multi‐agent systems with deception attacks. J Franklin Inst. 2020;357(6):3419‐3433.
[16]
He W, Mo Z, Han Q, Qian F. Secure impulsive synchronization in Lipschitz‐type multi‐agent systems subject to deception attacks. IEEE/CAA J Autom Sin. 2020;7(5):1326‐1334.
[17]
Wen G, Zhai X, Peng Z, Rahmani A. Fault‐tolerant secure consensus tracking of delayed nonlinear multi‐agent systems with deception attacks and uncertain parameters via impulsive control. Commun Nonlinear Sci Numer Simul. 2020;82:105043.
[18]
He W, Gao X, Zhong W, Qian F. Secure impulsive synchronization control of multi‐agent systems under deception attacks. Inform Sci. 2018;459:354‐368.
[19]
Yu Y, Guo C, Yu H. Finite‐time plos‐based integral sliding‐mode adaptive neural path following for unmanned surface vessels with unknown dynamics and disturbances. IEEE Trans Autom Sci Eng. 2019;16(4):1500‐1511.
[20]
Liao R, Han L, Dong X, Li Q, Ren Z. Finite‐time formation‐containment tracking for second‐order multi‐agent systems with a virtual leader of fully unknown input. Neurocomputing. 2020;415:234‐246.
[21]
Guan S, Pang H, Li Y. Finite‐time adaptive fuzzy consensus control for nonlinear multi‐agent systems under switching‐directed topologies. IET Control Theory Appl. 2022;36(12):3105‐3123.
[22]
Wang F, Zhang Y, Zhang L, Zhang L, Huang Y. Finite‐time consensus of stochastic nonlinear multi‐agent systems. Int J Fuzzy Syst. 2020;22(1):77‐88.
[23]
Wang Y, Song Y, Krstic M, Wen C. Fault‐tolerant finite time consensus for multiple uncertain nonlinear mechanical systems under single‐way directed communication interactions and actuation failures. Automatica. 2016;63:374‐383.
[24]
Sakthivel R, Parivallal A, Kaviarasan B, Lee H, Lim Y. Finite‐time consensus of Markov jumping multi‐agent systems with time‐varying actuator faults and input saturation. ISA Trans. 2018;83:89‐99.
[25]
Dong G, Li H, Ma H, Lu R. Finite‐time consensus tracking neural network FTC of multi‐agent systems. IEEE Trans Neural Netw Learn Syst. 2021;32(2):653‐662.
[26]
Song S, Park J, Zhang B, Song X. Adaptive NN finite‐time resilient control for nonlinear time‐delay systems with unknown false data injection and actuator faults. IEEE Trans Neural Netw Learn Syst. 2021;32(10):5416‐5428.
[27]
Olfati‐Saber R, Murray R. Consensus problems in networks of agents with switching topology and time‐delays. IEEE Trans Automat Contr. 2004;49(9):1520‐1533.
[28]
Bhat S, Bernstein D. Continuous finite‐time stabilization of the translational and rotational double integrators. IEEE Trans Automat Contr. 1998;43(5):678‐682.
[29]
Qian C, Lin W. Non‐Lipschitz continuous stabilizers for nonlinear systems with uncontrollable unstable linearization. Syst Control Lett. 2001;42(3):185‐200.
[30]
Hardy G, Littlewood J, Polya G. Inequalities. World Publishing Corporation; 2004.
[31]
Wang Y, Song Y, Lewis FL. Robust adaptive fault‐tolerant control of multiagent systems with uncertain nonidentical dynamics and undetectable actuation failures. IEEE Trans Ind Electron. 2015;62(6):3978‐3988.
[32]
Fu J, Wang J. Fixed‐time coordinated tracking for second‐order multi‐agent systems with bounded input uncertainties. Syst Control Lett. 2016;93:1‐12.
[33]
Polycarpou M, Ioannou P. A robust adaptive nonlinear control design. Automatica. 1996;32(3):423‐427.
[34]
Li T, Liu C, Wang X. Cooperative adaptive fault‐tolerant control for multi‐agent systems with deception attacks. 2021 40th Chinese Control Conference. IEEE; 2021:4516‐4521.
Index Terms
- Distributed finite‐time adaptive fault‐tolerant consensus control of second‐order multi‐agent systems under deception attacks
Index terms have been assigned to the content through auto-classification.
Recommendations
Quantized consensus control for second-order multi-agent systems with nonlinear dynamics
This paper discusses a leader-following consensus problem of second-order multi-agent systems with nonlinear dynamics and directed communication topology based on quantized information. The communication signals between the neighbor agents are quantized ...
Distributed consensus control for second-order nonlinear multi-agent systems with unknown control directions and position constraints
AbstractThis paper investigates the leaderless consensus problem in the presence of unknown control directions and position constraints under directed graph. Based on the Nussbaum-gain technique and Barrier Lyapunov functions, the position-...
Neural-network-based distributed adaptive asymptotically consensus tracking control for nonlinear multiagent systems with input quantization and actuator faults
AbstractThis paper investigates the consensus asymptotic convergence problem for a class of nth-order strict-feedback multiagent systems, which include the input quantization, actuator faults, unknown nonlinear functions and directed ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© 2024 John Wiley & Sons Ltd.
Publisher
John Wiley & Sons, Inc.
United States
Publication History
Published: 16 January 2024
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 11 Dec 2024
Other Metrics
Citations
View Options
View options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in