UAV group control protocol with adaptive consensus
Pages 3177 - 3194
Summary
In this paper, a modified consensus protocol algorithm is proposed for controlling a group of identical unmanned aerial vehicles (UAVs), which have been subjected to interfering signals during coordinate information exchange, using an unknown parameter in the consensus protocol. The maximum levels of interfering signals in the proposed protocol were adjusted by incorporating a hysteresis function with a dead zone consistent with the initial coordinates and the interfering signal levels. An adaptation algorithm is proposed to address a priori uncertainty regarding the consensus parameters, involving the correction of an unknown parameter by eliminating control signals exhibiting false sign changes. This correction relies on the coordinates in the phase plane, indicating that the delay in maneuver execution occurs at the beginning of the maneuver. Furthermore, by modeling synchronized motion, UAV group consensus is demonstrated for an ideal case devoid of a priori uncertainty regarding control protocol parameters, interfering signals, or consequences. The convergence of the adaptation algorithm was assessed by defining a vector function to track parameter changes during tuning. The monotonically decreasing nature of the resulting curve, along with the finite duration of the tuning process, provides confirmation of the convergence of the adaptation algorithm.
References
[1]
Tayebi A, McGilvray S. Attitude stabilization of a VTOL quadrotor aircraft. IEEE Trans Control Syst Technol. 2006;14(3):562‐571.
[2]
Mechali O, Iqbal J, Xie X, Xu L, Senouci A. Robust finite‐time trajectory tracking control of quadrotor aircraft via terminal sliding mode‐based active antidisturbance approach: a PIL experiment. Int J Aerosp Eng. 2021;2021:1‐28.
[3]
Schöllig A, Augugliaro F, Lupashin S, et al. Synchronizing the motion of a quadrocopter to music. 2010 IEEE International Conference on Robotics and Automation. IEEE, 2010: 3355–3360.
[4]
Jagannath J, Jagannath A, Furman S, Gwin T. Deep learning and reinforcement learning for autonomous unmanned aerial systems: roadmap for theory to deployment. In: Koubaa A, Azar AT, eds. Deep Learning for Unmanned Systems. Studies in Computational Intelligence, vol. 984. Springer; 2021:25‐82.
[5]
Lawton JRT, Beard RW, Young BJ. A decentralized approach to formation maneuvers. IEEE Trans Robot Autom. 2003;19(6):933‐941.
[6]
Olfati‐Saber R, Murray RM. Consensus problems in networks of agents with switching topology and time‐delays. IEEE Trans Automat Contr. 2004;49(9):1520‐1533.
[7]
Olfati‐Saber R. Flocking for multi‐agent dynamic systems: algorithms and theory. IEEE Trans Automat Contr. 2006;51(3):401‐420.
[8]
Ren W, Beard RW, McLain TW. Coordination variables and consensus building in multiple vehicle systems. Cooperative Control: A Post‐Workshop Volume 2003 Block Island Workshop on Cooperative Control. Springer Berlin Heidelberg, 2005: 171–188.
[9]
Ren W, Atkins E. Distributed multi‐vehicle coordinated control via local information exchange. Int J Robust Nonlinear Control. 2007;17(10–11):1002‐1033.
[10]
Zhang Q, Lapierre L, Xiang X. Distributed control of coordinated path tracking for networked nonholonomic mobile vehicles. IEEE Trans Industr Inform. 2012;9(1):472‐484.
[11]
Kushleyev A, Mellinger D, Powers C, Kumar V. Towards a swarm of agile micro quadrotors. Auton Robots. 2013;35(4):287‐300.
[12]
Saif O, Fantoni I, Zavala‐Río A. Real‐time flocking of multiple‐quadrotor system of systems. 2015 10th system of systems engineering conference (SoSE). IEEE, 2015: 286–291.
[13]
Zhang W, Wang Z, Guo Y, et al. Distributed cooperative spectrum sensing based on weighted average consensus. 2011 IEEE Global Telecommunications Conference‐GLOBECOM 2011. IEEE, 2011: 1–6.
[14]
Shang Y. Finite‐time weighted average consensus and generalized consensus over a subset. IEEE Access. 2016;4:2615‐2620.
[15]
Mo Y, Murray RM. Privacy preserving average consensus. IEEE Trans Automat Contr. 2016;62(2):753‐765.
[16]
Wang Y. Privacy‐preserving average consensus via state decomposition. IEEE Trans Automat Contr. 2019;64(11):4711‐4716.
[17]
Wang X, He J, Cheng P, Chen J. Privacy preserving collaborative computing: heterogeneous privacy guarantee and efficient incentive mechanism. IEEE Trans Signal Process. 2018;67(1):221‐233.
[18]
Zhong Y, Lv Y. Appointed‐weight Privacy‐preserving Consensus of Multi‐agent Systems. 2021 International Conference on Neuromorphic Computing (ICNC). IEEE, 2021: 297–301.
[19]
Duo QI, Junhua HU, Xiaolong L, et al. Research on consensus of multi‐agent systems with and without input saturation constraints. J Syst Eng Electr. 2021;32(4):947‐955.
[20]
Zhou J, Lv Y, Wen G, Chen G. Terminal‐time synchronization of multivehicle systems under sampled‐data communications. IEEE Trans Syst Man Cybern Syst. 2021;52(4):2625‐2636.
[21]
Lv Y, Wen G, Huang T, et al. Adaptive attack‐free protocol for consensus tracking with pure relative output information. Automatica. 2020;117:1‐11.
[22]
Lv Y, Wen G, Huang T. Adaptive protocol design for distributed tracking with relative output information: a distributed fixed‐time observer approach. IEEE Trans Control Netw Syst. 2019;7(1):118‐128.
[23]
He X, Yang C, Li Z, et al. Consensus control for single‐integrator multi‐agent systems with unknown control directions via event‐triggered approach. 2021 36th Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE, 2021: 757–760.
[24]
Wang J, Qiao J, Wen G, Duan Z, Huang T. Rendezvous of heterogeneous multiagent systems with nonuniform time‐varying information delays: an adaptive approach. IEEE Trans Syst Man Cybern Syst. 2019;51(8):4848‐4857.
[25]
Lee MY, Chen BS, Chang Y, Hwang CL. Stochastic robust team formation tracking design of multi‐VTOL‐UAV networked control system in smart city under time‐varying delay and random fluctuation. IEEE Access. 2020;8:131310‐131326.
[26]
Jayabalan J, Jeyanthi N. A study on distributed consensus protocols and algorithms: the backbone of blockchain networks. 2021 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2021, 1–10.
[27]
Jasim WM, Gu D. Integral Backstepping Controller for UAVs Team Formation. Automation and Control; 2020.
[28]
Babaie R, Ehyaei AF. Cooperative control of multiple quadrotors for transporting a common payload. AUT J Model Simul. 2018;50(2):147‐156.
[29]
Yang J, Wang X, Baldi S, Singh S, Farì S. A software‐in‐the‐loop implementation of adaptive formation control for fixed‐wing UAVs. IEEE/CAA J Automatica Sinica. 2019;6(5):1230‐1239.
[30]
Wang X, Baldi S, Feng X, Wu C, Xie H, de Schutter B. A fixed‐wing UAV formation algorithm based on vector field guidance. IEEE Trans Autom Sci Eng. 2022;20(1):179‐192.
[31]
Narendra KS, Annaswamy AM. Stable Adaptive Systems. Courier Corporation; 2012.
[32]
Kucherov DP. Group of UAVs moving on smooth control law with fixed obstacles. Adv Sci Technol Eng Syst J. 2017;2(3):1034‐1041.
[33]
Kucherov D, Fu M, Kozub A. Synthesis of the laws of motion control of a uav group with natural obstacles. Automated systems in the aviation and aerospace industries. IGI Global; 2019.
[34]
Kucherov DP. Synthesis of adaptive controller for fixed‐time control of a spinning body under the presence of bounded noise. J Autom Inf Sci. 2005;37(1):16‐25.
[35]
Bryson AE. Applied Optimal Control: Optimization, Estimation and Control. Routledge; 2018.
[36]
Shen Z, Andersson SB. Minimum time control of a second‐order system. 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010: 4819–4824.
[37]
Korn GA, Korn TM. Mathematical Handbook for Scientist and Engineering. Dover Publication; 2000, p. 1152.
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© 2024 John Wiley & Sons Ltd.
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John Wiley & Sons, Inc.
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Published: 01 September 2024
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