This paper focuses on the detailed illustration over the feedback robust control of high gain for flocking of the multi-agent system. As for the second-order unknown bounded nonlinear dynamic system, the designed controller has feedback robust control of high gain. Under the action of the leader, the flocking of the multi-agent system established on the basis of high gain feedback robust control can be realized. By employing the theory of Lyapunov stability, it is observed that the velocity error between agents approaches to zero, and no collision occurs between agents. It is further proved by the simulation that the high gain feedback robust control for flocking of multi-agent system can be obtained accordingly. Compared with Qing et al. (2014), high gain feedback robust control for flocking of multi-agent system has better stability.
CarliniNWagnerD (2017) Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy, San Jose, CA, USA, 22-26 May 2017, pp. 39–57. IEEE.
2.
ChenBChengHHPalenJ (2009) Integrating mobile agent technology with multi-agent systems for distributed traffic detection and management systems. Transportation Research Part C: Emerging Technologies17(1): 1–10.
3.
FangYLuG (2009) Nonlinear System Theory. Beijing: Tsinghua University Press.
4.
FerraraAIncremonaGP (2015) Design of an integral suboptimal second-order sliding mode controller for the robust motion control of robot manipulators. IEEE Transactions on Control Systems Technology23(6): 2316–2325.
5.
FerraraAVecchioC (2007) Collision avoidance strategies and coordinated control of passenger vehicles. Nonlinear Dynamics49(4): 475–492.
6.
GuinaldoMSánchezJDormidoS (2016) Distributed adaptive control of linear multi-agent systems with event-triggered communications. Applied Mathematics and Computation274: 195–207.
7.
HeWGeSSLiY, et al. (2015) Neural network control of a rehabilitation robot by state and output feedback. Journal of Intelligent & Robotic Systems80(1): 15–31.
8.
JadbabaieALinJMorseAS (2003) Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Transactions on Automatic Control48(6): 988–1001.
9.
LiXXiY (2008) Flocking of multi-agent dynamic systems with guaranteed group connectivity. Journal of Systems Science and Complexity21(3): 337–346.
10.
Olfati-SaberRMurrayR M (2003) Flocking with obstacle avoidance: Cooperation with limited communication in mobile networks. In:42nd IEEE International Conference on Decision and Control2, Maui, HI, USA, 9–12 Dec. 2003, pp. 2022–2028. IEEE.
11.
Olfati-SaberRMurrayR M (2004) Consensus problems in networks of agents with switching topology and time-delays. IEEE Transactions on Automatic Control49(9): 1520–1533.
12.
OnarSCOztaysiBOtayİKahramanC (2015) Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets. Energy90: 274–285.
13.
WangQiangChenJieFangHaoMaQian (2014) Flocking control for multi-agent systems with stream-based obstacle avoidance. Transactions of the Institute of Measurement and Control36(3): 391–398.
14.
QingZPingLZhengquan (2014) Adaptive flocking of multi-agents with nonlinear inner-coupling function. ICIC Express Letters Part B Applications: An International Journal of Research & Surveys 5(5): 1445–1452.
15.
SaulnierKSaldanaDProrokAPappasG JKumarV (2017) Resilient flocking for mobile robot teams. IEEE Robotics and Automation Letters2(2): 1039–1046.
16.
TannerHGJadbabaieAPappasGJ (2005) Flocking in teams of nonholonomic agents. Lecture Notes in Control & Information Sciences309: 229–339.
17.
TonerJTuY (1998) Flocks, herds, and schools: A quantitative theory of flocking. Physical Review E58(4): 4828–4858.
18.
VicsekTCzirókABen-JacobE, et al. (1995) Novel type of phase transition in a system of self-driven particles. Physical Review Letters75(6): 1226–1229.
19.
WangLLinFYinG (2016) Network robustness depth and topology management of networked dynamic systems. Journal of Systems Science and Complexity29(1): 1–21.
20.
YangZLiuZChenZYuanZ (2008) Tracking control for multi-agent consensus with an active leader and directed topology. In: 2008 7th World Congress on Intelligent Control and Automation, Chongqing, China, 25–27 June 2008, pp. 1037–1041. IEEE.
21.
ZavlanosMMJadbabaieAPappasG J (2007) Flocking while preserving network connectivity. In: 2007 46th IEEE Conference on Decision and Control, New Orleans, LA, USA, 12–14 Dec. 2007, pp. 2919–2924. IEEE.
22.
ZhangbaoXuDaweiMaJianyongYao (2018) Uniform robust exact differentiator based adaptive robust control for a class of nonlinear systems. Transactions of the Institute of Measurement and Control40(9): 2901–2911.
