Distributed secure consensus control of nonlinear multi-agent systems under sensor and actuator attacks

被引:9
作者
Gorbachev, Sergey [1 ]
Yang, Yang [2 ,3 ]
Liu, Qidong [2 ,3 ]
Ge, Jingzhi [2 ,3 ]
Yue, Dong [2 ,3 ]
Mani, Ashish [1 ]
Shevchuk, Dmytro [1 ]
机构
[1] Chongqing Univ Educ, Sch Artificial Intelligence, Chongqing 400065, Peoples R China
[2] Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing 210023, Peoples R China
[3] Nanjing Univ Posts & Telecommun, Coll Artificial Intelligence, Nanjing 210023, Peoples R China
来源
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2023年 / 360卷 / 11期
关键词
CYBER-PHYSICAL SYSTEMS; DYNAMIC SURFACE CONTROL; DATA INJECTION ATTACKS; DEAD-ZONE; TRACKING; AGENTS;
D O I
10.1016/j.jfranklin.2023.05.024
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we focus on an output secure consensus control issue for nonlinear multi-agent systems (MASs) under sensor and actuator attacks. Followers in an MAS are in strict-feedback form with unknown control directions and unknown dead-zone input, where both sensors and nonlinear characteristics of dead-zone in actuators are paralyzed by malicious attacks. To deal with sensor attacks, uncertain dynamics in individual follower are separated by a separation theorem, and estimation parameters are introduced for compensating and mitigating the influence from adversaries. The influence from actuator attacks are treated as a total displacement in a dead-zone nonlinearity, and an upper bound, as well as its estimation, is introduced for this displacement. The dead-zone nonlinearity, sensor attacks and unknown control gains are gathered together regarded as composite unknown control directions, and Nussbaum functions are utilized to address the issue of unknown control directions. A distributed secure consensus control strategy is thus developed recursively for each follower in the framework of surface control method. Theoretically, the stability of the closed-loop MAS is analyzed, and it is proved that the MAS achieves output consensus in spite of nonlinear dynamics and malicious attacks. Finally, theoretical results are verified via a numerical example and a group of electromechanical systems. & COPY; 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.
引用
收藏
页码:7501 / 7534
页数:34
相关论文
共 44 条
[11]   Distributed Adaptive Neural Network Output Tracking of Leader-Following High-Order Stochastic Nonlinear Multiagent Systems With Unknown Dead-Zone Input [J].
Hua, Changchun ;
Zhang, Liuliu ;
Guan, Xinping .
IEEE TRANSACTIONS ON CYBERNETICS, 2017, 47 (01) :177-185
[12]   An Adaptive Secure Control Scheme for T-S Fuzzy Systems Against Simultaneous Stealthy Sensor and Actuator Attacks [J].
Huang, Xin ;
Dong, Jiuxiang .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2021, 29 (07) :1978-1991
[13]   Adaptive Fault-Tolerant Consensus for a Class of Uncertain Nonlinear Second-Order Multi-Agent Systems With Circuit Implementation [J].
Jin, Xiaozheng ;
Wang, Shaofan ;
Qin, Jiahu ;
Zheng, Wei Xing ;
Kang, Yu .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2018, 65 (07) :2243-2255
[14]   Fuzzy Adaptive Fault-Tolerant Control for Uncertain Nonlinear Systems With Unknown Dead-Zone and Unmodeled Dynamics [J].
Jing, Yan-Hui ;
Yang, Guang-Hong .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2019, 27 (12) :2265-2278
[15]   Fuzzy adaptive high-gain-based observer backstepping control for SISO nonlinear systems [J].
Li, Changying ;
Tong, Shaocheng ;
Wang, Wei .
INFORMATION SCIENCES, 2011, 181 (11) :2405-2421
[16]   A DSC Approach to Robust Adaptive NN Tracking Control for Strict-Feedback Nonlinear Systems [J].
Li, Tie-Shan ;
Wang, Dan ;
Feng, Gang ;
Tong, Shao-Cheng .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2010, 40 (03) :915-927
[17]   Distributed Fault-Tolerant Containment Control Protocols for the Discrete-Time Multiagent Systems via Reinforcement Learning Method [J].
Li, Tieshan ;
Bai, Weiwei ;
Liu, Qi ;
Long, Yue ;
Chen, C. L. Philip .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (08) :3979-3991
[18]   Adaptive control of nonlinearly parameterized systems: The smooth feedback case [J].
Lin, W ;
Qian, CJ .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2002, 47 (08) :1249-1266
[19]   Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input [J].
Liu, Yan-Jun ;
Li, Shu ;
Tong, Shaocheng ;
Chen, C. L. Philip .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (01) :295-305
[20]   Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone [J].
Liu, Yan-Jun ;
Gao, Ying ;
Tong, Shaocheng ;
Li, Yongming .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2016, 24 (01) :16-28