基于中间状态值的多智能体系统安全一致性控制
|
摘要
针对存在恶意攻击的多智能体系统一致性控制问题,提出一种快捷有效的安全一致性算法.采用选取中间值的筛选方法,将同一时刻采集到的邻居信息值按从小到大序列排序,选取位于中间序列的信息值用于节点自身的状态更新,该算法较传统一致性算法减少了计算复杂度,同时降低了系统所需较强的网络连通条件和信息储备所需的资源,使得整个系统变得更加简单、灵活.利用迭代学习和凸包条件,通过创建具有与原系统有向图相同连通条件的虚拟网络拓扑图,证明了系统在满足特定的网络拓扑的条件下,能够实现安全一致.仿真结果验证了所提出算法的有效性.
For consensus control of multi-agent systems under malicious attacks, a fast and effective secure consensus algorithm is proposed. In each status update process, normal agents select the median state value in an arranged value sequence collected at the same time from their neighbor agents. The computation complexity is reduced. The strong network connectivity and vast resource needed for information storage of the traditional algorithm are also reduced. The proposed algorithm makes the whole system much simple, flexible, and cheap. By using iterative learning and convex hull conditions, we create a virtual network topology with the same connectivity condition as the original system directed graph. Under the condition that the system satisfies certain network topology, it is proved that the system is safe and consistent. Simulation results verify the effectiveness of the proposed algorithm.
For consensus control of multi-agent systems under malicious attacks, a fast and effective secure consensus algorithm is proposed. In each status update process, normal agents select the median state value in an arranged value sequence collected at the same time from their neighbor agents. The computation complexity is reduced. The strong network connectivity and vast resource needed for information storage of the traditional algorithm are also reduced. The proposed algorithm makes the whole system much simple, flexible, and cheap. By using iterative learning and convex hull conditions, we create a virtual network topology with the same connectivity condition as the original system directed graph. Under the condition that the system satisfies certain network topology, it is proved that the system is safe and consistent. Simulation results verify the effectiveness of the proposed algorithm.
引文
[1]王祥科,李迅,郑志强.多智能体系统编队控制相关问题研究综述[J].控制与决策, 2013, 28(11):1601-1613.(Wang X K, Li X, Zheng Z Q. Survey of developments on multi-agent formation control related problems[J].Control and Decision, 2013, 28(11):1601-1613.)
[2] Zolfpour-Arokhlo M, Mashinchi M R. A multi-agent system approach to control road transportation network[C]. Swarm Intelligence and Evolutionary Computation. Bam, 2016:42-46.
[3] Grivault L, Fallahseghrouchni A E, Girardclaudon R. Agent-based architecture for multi-sensors system deployed on airborne platform[C]. IEEE Int Conf on Agents. Matsue, 2016:86-89.
[4] Cha H J, Won D J, Kim S H, et al. Multi-agent system-based microgrid operation strategy for demand response[J]. Energies, 2015, 8(12):14272-14286.
[5] Dolev D, Lynch N A, Pinter S S, et al. Reaching approximate agreement in the presence of faults[J]. J of the ACM, 1986, 33(3):499-516.
[6] Sundaram S, Hadjicostis C N. Distributed function calculation via linear iterative strategies in the presence of malicious agents[J]. IEEE Trans on Automatic Control,2011, 56(7):1495-1508.
[7] Kieckhafer R M, Azadmanesh M H. Reaching approximate agreement with mixed-mode faults[J]. IEEE Trans on Parallel and Distributed Systems, 1994, 5(1):53-63.
[8] Wu Y M, He X X, Liu S, et al. Consensus of discrete-time multi-agent systems with adversaries and time delays[J].Int J of General Systems, 2014, 43(3/4):402-411.
[9] Wu Y M, He X X. Secure consensus control for multi-agent systems with attacks and communication delays[J]. IEEE/CAA J of Automatica Sinica, 2017, 4(1):136-142.
[10] Abbas W, Vorobeychik Y, Koutsoukos X. Resilient consensus protocol in the presence of trustednodes[C].Proc of the 7th Int Symposium on Resilient Control Systems. Denver, 2014:1-7.
[11]伍巧凤,刘山.初始误差修正的多智能体一致性迭代学习控制[J].计算机工程与应用, 2014, 50(1):29-35.(Wu Q F, Liu S. Iterative learning control of multi-agent consensus with initial error correction[J]. Computer Engineering and Applications, 2014, 50(1):29-35.)
[12] Xu J. Adaptive iterative learning control for high-order nonlinear multi-agent systems consensus tracking[J].Systems&Control Letters, 2016, 89(1):16-23.
[13] Liu X, Lam J, Yu W, et al. Finite-time consensus of multiagent systems with a switching protocol[J]. IEEE Trans on Neural Networks and Learning Systems, 2016,27(4):853-862.
[14] West D B. Introduction to graph theory[M]. New Jersey:Prentice Hall, 2001:1-190.
[15] Godsil C, Royle G F. Algebraic graph theory[M]. New York:Springer-Verlag, 2001:9-72.
[16] LeBlanc H J, Zhang H, Sundaram S, et al.Resilient continuous-time consensus in fractional robust networks[C]. Proc of the American Control Conf.Washington DC, 2013:1237-1242.
[17] Ren W, Beard R W. Consensus seeking in multiagent systems under dynamically changing interaction topologies[J]. IEEE Trans on Automatic Control, 2005,50(5):655-661.
[18] Wolfowitz J. Products of indecomposable, aperiodic,stochastic matrices[J]. Proc of the American Mathematical Society, 1963, 14(5):733-737.
[19] Zuckerberg M. Geometric proofs for convex hull defining formulations[J]. Operations Research Letters, 2016,44(5):625-629.
[2] Zolfpour-Arokhlo M, Mashinchi M R. A multi-agent system approach to control road transportation network[C]. Swarm Intelligence and Evolutionary Computation. Bam, 2016:42-46.
[3] Grivault L, Fallahseghrouchni A E, Girardclaudon R. Agent-based architecture for multi-sensors system deployed on airborne platform[C]. IEEE Int Conf on Agents. Matsue, 2016:86-89.
[4] Cha H J, Won D J, Kim S H, et al. Multi-agent system-based microgrid operation strategy for demand response[J]. Energies, 2015, 8(12):14272-14286.
[5] Dolev D, Lynch N A, Pinter S S, et al. Reaching approximate agreement in the presence of faults[J]. J of the ACM, 1986, 33(3):499-516.
[6] Sundaram S, Hadjicostis C N. Distributed function calculation via linear iterative strategies in the presence of malicious agents[J]. IEEE Trans on Automatic Control,2011, 56(7):1495-1508.
[7] Kieckhafer R M, Azadmanesh M H. Reaching approximate agreement with mixed-mode faults[J]. IEEE Trans on Parallel and Distributed Systems, 1994, 5(1):53-63.
[8] Wu Y M, He X X, Liu S, et al. Consensus of discrete-time multi-agent systems with adversaries and time delays[J].Int J of General Systems, 2014, 43(3/4):402-411.
[9] Wu Y M, He X X. Secure consensus control for multi-agent systems with attacks and communication delays[J]. IEEE/CAA J of Automatica Sinica, 2017, 4(1):136-142.
[10] Abbas W, Vorobeychik Y, Koutsoukos X. Resilient consensus protocol in the presence of trustednodes[C].Proc of the 7th Int Symposium on Resilient Control Systems. Denver, 2014:1-7.
[11]伍巧凤,刘山.初始误差修正的多智能体一致性迭代学习控制[J].计算机工程与应用, 2014, 50(1):29-35.(Wu Q F, Liu S. Iterative learning control of multi-agent consensus with initial error correction[J]. Computer Engineering and Applications, 2014, 50(1):29-35.)
[12] Xu J. Adaptive iterative learning control for high-order nonlinear multi-agent systems consensus tracking[J].Systems&Control Letters, 2016, 89(1):16-23.
[13] Liu X, Lam J, Yu W, et al. Finite-time consensus of multiagent systems with a switching protocol[J]. IEEE Trans on Neural Networks and Learning Systems, 2016,27(4):853-862.
[14] West D B. Introduction to graph theory[M]. New Jersey:Prentice Hall, 2001:1-190.
[15] Godsil C, Royle G F. Algebraic graph theory[M]. New York:Springer-Verlag, 2001:9-72.
[16] LeBlanc H J, Zhang H, Sundaram S, et al.Resilient continuous-time consensus in fractional robust networks[C]. Proc of the American Control Conf.Washington DC, 2013:1237-1242.
[17] Ren W, Beard R W. Consensus seeking in multiagent systems under dynamically changing interaction topologies[J]. IEEE Trans on Automatic Control, 2005,50(5):655-661.
[18] Wolfowitz J. Products of indecomposable, aperiodic,stochastic matrices[J]. Proc of the American Mathematical Society, 1963, 14(5):733-737.
[19] Zuckerberg M. Geometric proofs for convex hull defining formulations[J]. Operations Research Letters, 2016,44(5):625-629.