Trophallaxis network control approach to formation flight of multiple unmanned aerial vehicles

A novel network control method based on trophallaxis mechanism is applied to the formation flight problem for multiple unmanned aerial vehicles (UAVs). Firstly, the multiple UAVs formation flight system based on trophallaxis network control is given. Then, the model of leader-follower formation flight with a virtual leader based on trophallaxis network control is presented, and the influence of time delays on the network performance is analyzed. A particle swarm optimization (PSO)-based formation controller is proposed for solving the leader-follower formation flight system. The proposed method is applied to five UAVs for achieving a ‘V’ formation, and a series of experimental results show its feasibility and validity. The proposed control algorithm is also a promising control strategy for formation flight of multiple unmanned underwater vehicles (UUVs), unmanned ground vehicles (UGVs), missiles and satellites.

[1]  Derek James Bennet,et al.  Autonomous three-dimensional formation flight for a swarm of unmanned aerial vehicles , 2011 .

[2]  Pei Li,et al.  Progress in control approaches for hypersonic vehicle , 2012 .

[3]  James M. Hereford.  Analysis of a new swarm search algorithm based on trophallaxis , 2010, IEEE Congress on Evolutionary Computation.

[4]  Henrik Schiøler,et al.  Trophallaxis in robotic swarms - beyond energy autonomy , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[5]  Haibin Duan,et al.  Pendulum-like oscillation controller for micro aerial vehicle with ducted fan based on LQR and PSO , 2013 .

[6]  S. Q. Liu,et al.  Non-linear dual-mode receding horizon control for multiple unmanned air vehicles formation flight based on chaotic particle swarm optimisation , 2010 .

[7]  Ian F. Akyildiz,et al.  Efficient Recovery Control Channel Design in Cognitive Radio Ad Hoc Networks , 2010, IEEE Transactions on Vehicular Technology.

[8]  Tong Heng Lee,et al.  Hybrid formation control of the Unmanned Aerial Vehicles , 2011 .

[9]  Haibin Duan,et al.  Receding horizon control for multi-UAVs close formation control based on differential evolution , 2010, Science China Information Sciences.

[10]  T. Schmickl,et al.  Trophallaxis among swarm-robots: A biologically inspired strategy for swarm robotics , 2006, The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006..

[11]  Chongzhao Han,et al.  Binary particle swarm optimization with multiple evolutionary strategies , 2011, Science China Information Sciences.

[12]  C. Melhuish,et al.  Robot Trophallaxis : Managing Energy Autonomy in Multiple Robots , 2004 .

[13]  L. Bushnell.  Networks and Control , 2001 .

[14]  Li Qiu,et al.  H2/H∞ control of networked control system with random time delays , 2011, Science China Information Sciences.

[15]  Nan Wang,et al.  Formation control for networked spacecraft in deep space: with or without communication delays and with switching topology , 2011, Science China Information Sciences.

[16]  Delin Luo,et al.  Optimal Formation Reconfiguration Control of Multiple UCAVs Using Improved Particle Swarm Optimization , 2008 .

[17]  Kai Liu,et al.  Control allocation for a V/STOL aircraft based on robust fuzzy control , 2011, Science China Information Sciences.

[18]  Antonios Tsourdos,et al.  Terrain based co-operative UAV mapping of complex obstacles using 2-D splinegon , 2012, Int. J. Intell. Comput. Cybern..

[19]  James Moyne,et al.  Performance evaluation of control networks: Ethernet, ControlNet, and DeviceNet , 2001 .

[20]  Linda Bushnell,et al.  Networks and control [Guest Editorial] , 2001 .

[21]  R.S. Raji,et al.  Smart networks for control , 1994, IEEE Spectrum.

[22]  Haibin Duan,et al.  New development thoughts on the bio-inspired intelligence based control for unmanned combat aerial vehicle , 2010 .