Guidance and distributed control of the networked UAVs

Document Type : Original Article

Authors

1 ,

2 Instructor, Department of Electrical Engineering, Khatam ol anbia university

3 sahand university of technology

Abstract

he control and guidance ability of unmanned aerial vehicles (UAVs), as one of the modern tools of aerospace systemstechnology, has become an important priority in the air defense field of any country. In this paper, a group of the networked UAVs is considered that follow closely the specifiedobjectives.During the mission,the UAVs communicate with each other and exchange important information such as their velocities and positions with other agents in the network. Therefore, the controller is designed as a distributed approach. Due to the networked nature of this system, to reduce the computational load, the distributed optimization algorithm and model predictive control method are utilized to implement the network structure. In the following, many challenges such as keeping the desired arrangement, communication delay and optimal energy consumption are raised during the operation to reach the mission purpose. It is necessary that the designed controller holds the appropriate performance in the presence of the aforementioned challenges. Finally, simulations are carried out in the MATLAB software for investigating the performance of the proposed approach. The achieved results, despite the mentioned challenges in the networked systems, indicate a higher rate of convergence and a better ability to delay tolerance compared to the previous methods.

Keywords

References

[1]     M. A. Goodrich, B. S. Morse, C. Engh, J. L. Cooper, and J. A. J. I. S. Adams, “Towards using unmanned aerial vehicles (UAVs) in wilderness search and rescue: Lessons from field trials,” vol. 10, no. 3, pp. 453-478, 2009.##
[2]             S. J. A. P. Bartczak, “Identifying barriers to knowledge management in the United States military,” p. 343, 2002.##
[3]             X. Ge, F. Yang, and Q.-L. J. I. S. Han, “Distributed networked control systems: A brief overview,” vol. 380, pp. 117-131, 2017.##
[4]             W. Ren, “Consensus seeking, formation keeping, and trajectory tracking in multiple vehicle cooperative control,” 2004.##
[5]             Q. Yang, M. Cao, H. G. de Marina, H. Fang, J. J. S. Chen, and C. Letters, “Distributed formation tracking using local coordinate systems,” vol. 111, pp. 70-78, 2018.##
[6]             H. Yu, P. Shi, C.-C. Lim, and D. Wang, “Formation control for multi-robot systems with collision avoidance,” International Journal of Control, pp. 1-12, 2018.##
[7]             J. Qin, Q. Ma, Y. Shi, and L. J. I. T. o. I. E. Wang, “Recent advances in consensus of multi-agent systems: A brief survey,” vol. 64, no. 6, pp. 4972-4983, 2017.##
[8]             K.-K. Oh, M.-C. Park, and H.-S. J. A. Ahn, “A survey of multi-agent formation control,” vol. 53, pp. 424-440, 2015.##
[9]             M. Peymankar, P. Ranjbar, A. Izadipour  and S. Balouchian, “Modelling and Solving the Location Problem of Fire Launching Sites %J Electronic and Cyber Defense,” vol. 6, no. 3, pp. 45-57, 2018.##
[10]          A. Nedic and A. Ozdaglar, “Distributed Subgradient Methods for Multi-Agent Optimization,” IEEE Transactions on Automatic Control, vol. 54, no. 1, pp. 48-61, 2009.##
[11]          I. Lobel and A. Ozdaglar, “Distributed subgradient methods for convex optimization over random networks,” IEEE Transactions on Automatic Control, vol. 56, no. 6, pp.   1291-1306, 2011.##
[12]          H. Terelius, U. Topcu, and R. M. Murray, “Decentralized multi-agent optimization via dual decomposition,” IFAC Proceedings Volumes, vol. 44, no. 1, pp. 11245-11251, 2011.##
[13]          F. Farokhi, I. Shames, and K. H. Johansson, “Distributed MPC via dual decomposition and alternative direction method of multipliers, in Distributed model predictive control made easy: Springer, 2014, pp. 115-131.##
[14]          G. N. Droge, “Behavior-based model predictive control for networked multi-agent systems,” Georgia Institute of Technology, 2014.##
[15]          J.-P. Richard, “Time-delay systems: an overview of some recent advances and open problems,” automatica, vol. 39, no. 10, pp. 1667-1694, 2003.##
[16]          M. G. Rabbat and R. D. Nowak, “Quantized incremental algorithms for distributed optimization,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 4, pp.     798-808, 2005.##
[17]          S.-I. Niculescu, “Delay effects on stability: a robust control approach,” Springer Science & Business Media, 2001.##
[18]          W. Xu, J. Cao, M. Xiao, D. W. Ho, and G. Wen, “A new framework for analysis on stability and bifurcation in a class of neural networks with discrete and distributed delays,” IEEE transactions on cybernetics, vol. 45, no. 10, pp.     2224-2236, 2015.##
[19]          J. Li, G. Li, Z. Wu, and C. J. O. L. Wu, “Stochastic mirror descent method for distributed multi-agent optimization,” vol. 12, no. 6, pp. 1179-1197, 2018.##
[20] 
129
        T. Hatanaka, N. Chopra, T. Ishizaki, and N. J. I. T. o. A. C. Li, “Passivity-based distributed optimization with communication delays using PI consensus algorithm,” vol. 63, no. 12, pp. 4421-4428, 2018.##
[21]          S. Yang, Q. Liu, J. J. I. T. S. Wang, Man, and C. Systems, “Distributed Optimization Based on a Multiagent System in the Presence of Communication Delays,” vol. 47, no. 5, pp. 717-728, 2017.##
[22]          M. R. Davoodi et al., “An Overview of Cooperative and Consensus Control of Multiagent Systems,” pp. 1-35, 1999.##
[23]          A. Abdessameud and A. J. A. Tayebi, “Formation control of VTOL unmanned aerial vehicles with communication delays,” vol. 47, no. 11, pp. 2383-2394, 2011.##
[24]          J. Zhou, Q. Hu, Y. Zhang, and G. Ma, “Decentralised adaptive output feedback synchronisation tracking control of spacecraft formation flying with time-varying delay,” IET Control Theory & Applications, vol. 6, no. 13, pp.         2009-2020, 2012.##
[25] 
129
R. Wang and J. J. C. J. o. A. Liu, “Adaptive formation control of quadrotor unmanned aerial vehicles with bounded control thrust,” vol. 30, no. 2, pp. 807-817, 2017.##
[26]          Y. Zou, Z. Zhou, X. Dong, and Z. J. I. A. T. o. M. Meng, “Distributed formation control for multiple vertical takeoff and landing UAVs with switching topologies,” vol. 23, no. 4, pp. 1750-1761, 2018.##
[27]          P. Lin, W. Ren, and Y. J. A. Song, “Distributed multi-agent optimization subject to nonidentical constraints and communication delays,” vol. 65, pp. 120-131, 2016.##
[28]          D.-H. Kim and J.-H. Kim, “A real-time limit-cycle navigation method for fast mobile robots and its application to robot soccer,” Robotics and Autonomous Systems, vol. 42, no. 1, pp. 17-30, 2003.##

Files

History

  • Receive Date: 15 December 2018
  • Revise Date: 10 July 2019
  • Accept Date: 18 June 2019
  • Publish Date: 20 February 2020

Share

How to cite

Statistics