Adaptive NN leader-following consensus control of second-order nonlinear multi-agent systems with unknown control gains

This thesis investigate the consensus tracking control problem for second-order MAS with unknown control gains, unknown non-linear dynamics and external disturbance. Only part of followers can acquire the state information of the leader. RBFNNs are introduced to estimate the unknown non-linear function of agents dynamics. Based on Lyapunov theory and Nussbaum type function, a new consensus tracking control strategy is proposed by using only the communication of each agent and its vicinages. In this paper, it also proves that the second-order MAS can obtain consensus tracking control by selecting the appropriate parameters. Finally, simulation results verify the accuracy of the proposed consensus control method.

[1]  Zhengtao Ding.  Nonlinear and Adaptive Control Systems , 2013 .

[2]  Guo-Xing Wen,et al.  Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Long Cheng,et al.  Decentralized Robust Adaptive Control for the Multiagent System Consensus Problem Using Neural Networks , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Alexander Schwab.  Cooperative control of dynamical systems , 2017 .

[5]  Wenjie Dong,et al.  On consensus algorithms of multiple uncertain mechanical systems with a reference trajectory , 2011, Autom..

[6]  Wenjing Xie,et al.  Average consensus control of nonlinear uncertain multiagent systems , 2017, 2017 36th Chinese Control Conference (CCC).

[7]  Qian Ma,et al.  Cooperative control of multi-agent systems with unknown control directions , 2017, Appl. Math. Comput..

[8]  Tao Zhang,et al.  Stable Adaptive Neural Network Control , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[9]  Shaocheng Tong,et al.  Adaptive Neural Output Feedback Controller Design With Reduced-Order Observer for a Class of Uncertain Nonlinear SISO Systems , 2011, IEEE Transactions on Neural Networks.

[10]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[11]  Frank L. Lewis,et al.  Distributed adaptive control for synchronization of unknown nonlinear networked systems , 2010, Autom..

[12]  Zhengtao Ding,et al.  Adaptive consensus output regulation of a class of nonlinear systems with unknown high-frequency gain , 2015, Autom..

[13]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[14]  Yan-Jun Liu,et al.  Formation Control With Obstacle Avoidance for a Class of Stochastic Multiagent Systems , 2018, IEEE Transactions on Industrial Electronics.

[15]  Zhiping Shi,et al.  Disturbance Observer-Based Consensus Control for Multiple Robotic Manipulators , 2018, IEEE Access.

[16]  C. L. Philip Chen,et al.  Adaptive Fuzzy Leader-Following Consensus Control for Stochastic Multiagent Systems with Heterogeneous Nonlinear Dynamics , 2017, IEEE Transactions on Fuzzy Systems.

[17]  Randal W. Beard,et al.  Distributed Consensus in Multi-vehicle Cooperative Control - Theory and Applications , 2007, Communications and Control Engineering.

[18]  Abhijit Das,et al.  Cooperative Control of Multi-Agent Systems , 2014 .

[19]  Shaocheng Tong,et al.  Adaptive Neural Output Feedback Tracking Control for a Class of Uncertain Discrete-Time Nonlinear Systems , 2011, IEEE Transactions on Neural Networks.

[20]  Zhiping Shi,et al.  Distributed Observer-Based Leader-Following Consensus Control for Second-Order Stochastic Multi-Agent Systems , 2018, IEEE Access.