Backstepping approach to a class of hierarchical multi-agent systems with communication disturbance

In this study, mathematical models of hierarchical multi-agent systems (HMSs) are first proposed to demonstrate the hierarchical structure of multi-agent systems. Communication disturbance is also considered in HMSs since disturbance often appears when information is transmitted among agents due to various uncertainties such as model unsteadiness and external noise. Radial basis function neural networks are applied to approximate the non-linear functions of the communication disturbance. Then, by applying a backstepping method based on the hierarchical structure of HMSs, a simple condition is derived to ensure the stability of HMSs with disturbance.

[1]  Tao Li,et al.  Consensus Conditions of Multi-Agent Systems With Time-Varying Topologies and Stochastic Communication Noises , 2010, IEEE Transactions on Automatic Control.

[2]  Tianping Chen,et al.  Pinning Complex Networks by a Single Controller , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[3]  Andrea Lancichinetti,et al.  Detecting the overlapping and hierarchical community structure in complex networks , 2008, 0802.1218.

[4]  Peng Shi,et al.  Robust backstepping control for a class of time delayed systems , 2005, IEEE Transactions on Automatic Control.

[5]  Xiao Fan Wang,et al.  Decentralized Adaptive Pinning Control for Cluster Synchronization of Complex Dynamical Networks , 2013, IEEE Transactions on Cybernetics.

[6]  Tao Li,et al.  Consensus control for leader-following multi-agent systems with measurement noises , 2010, J. Syst. Sci. Complex..

[7]  Xinghuo Yu,et al.  On the cluster consensus of discrete-time multi-agent systems , 2011, Syst. Control. Lett..

[8]  M. Mehdi Afsar,et al.  Clustering in sensor networks: A literature survey , 2014, J. Netw. Comput. Appl..

[9]  Wenwu Yu,et al.  Second-Order Consensus for Multiagent Systems With Directed Topologies and Nonlinear Dynamics , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Meiqiang Feng,et al.  Positive solutions for a class of 2nth-order singular boundary value problems , 2008 .

[11]  Gang Feng,et al.  Synchronization of Complex Dynamical Networks With Time-Varying Delays Via Impulsive Distributed Control , 2010, IEEE Transactions on Circuits and Systems I: Regular Papers.

[12]  Albert-László Barabási,et al.  Scale‐Free and Hierarchical Structures in Complex Networks , 2003 .

[13]  Jinde Cao,et al.  Second-order leader-following consensus of nonlinear multi-agent systems via pinning control , 2010, Syst. Control. Lett..

[14]  Junmin Li,et al.  Adaptive neural control for a class of nonlinearly parametric time-delay systems , 2005, IEEE Transactions on Neural Networks.

[15]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[16]  Randal W. Beard,et al.  Consensus seeking in multiagent systems under dynamically changing interaction topologies , 2005, IEEE Transactions on Automatic Control.

[17]  M. Newman,et al.  Hierarchical structure and the prediction of missing links in networks , 2008, Nature.

[18]  Shengyuan Xu,et al.  Robust H∞ filtering for uncertain impulsive stochastic systems under sampled measurements , 2003, Autom..

[19]  M. Degroot.  Reaching a Consensus , 1974 .

[20]  Zidong Wang,et al.  Distributed fault detection for a class of second-order multi-agent systems: an optimal robust observer approach , 2014 .

[21]  Cheng-Chew Lim,et al.  Adaptive neural observer-based backstepping fault tolerant control for near space vehicle under control effector damage , 2014 .

[22]  Mireille E. Broucke,et al.  A hierarchical cyclic pursuit scheme for vehicle networks , 2005, Autom..

[23]  Johan A. K. Suykens,et al.  Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.

[24]  Xiang Li,et al.  Pinning a complex dynamical network to its equilibrium , 2004, IEEE Trans. Circuits Syst. I Regul. Pap..

[25]  Sung Jin Yoo,et al.  Distributed Consensus Tracking for Multiple Uncertain Nonlinear Strict-Feedback Systems Under a Directed Graph , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Biao Huang,et al.  Sampled-data filtering with error covariance assignment , 2001, IEEE Trans. Signal Process..

[27]  Zidong Wang,et al.  Sampled-Data Synchronization Control of Dynamical Networks With Stochastic Sampling , 2012, IEEE Transactions on Automatic Control.

[28]  Albert-László Barabási,et al.  Hierarchical organization in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Z. Duan,et al.  Dynamic consensus of linear multi-agent systems , 2011 .

[30]  Okyay Kaynak,et al.  Robust and adaptive backstepping control for nonlinear systems using RBF neural networks , 2004, IEEE Transactions on Neural Networks.

[31]  Yingmin Jia,et al.  Robust h ∞ consensus analysis of a class of second-order multi-agent systems with uncertainty , 2010 .