In this paper, the formation tracking control for multiple unmanned aerial vehicles (UAVs) is investigated with monotone prescribed performance control (MPPC). In order to reduce the amount of overshooting and convergence time, a novel distributed monotonic performance control scheme is proposed. Compared with the traditional prescribed performance control (PPC), the lower and upper bounds of proposed prescribed performance function are unsymmetric, which can enhance the transient performance. Moreover, an error conversion function is constructed by the MPPC to achieve the transforming of the formation tracking errors. In addition, based on second-order sliding mode differentiator and fuzzy logic system, the fully distributed adaptive formation control protocol is proposed to achieve predefined formation. Simulation is performed to demonstrate the effectiveness of the proposed control design in formation tracking control.
AnBWangBFanH, et al. (2022) Fully distributed prescribed performance formation control for UAVs with unknown maneuver of leader. Aerospace Science and Technology130: 107886.
2.
AskariAMortazaviMTalebiHA (2015) UAV formation control via the virtual structure approach. Journal of Aerospace Engineering28(1): 351.
3.
ChangSWangYZuoZ, et al. (2021) Fixed-time formation control for wheeled mobile robots with prescribed performance. IEEE Transactions on Control Systems Technology30(2): 844–851.
4.
ChenYDengT (2023) Leader-follower UAV formation flight control based on feature modelling. Systems Science & Control Engineering11(1): 2268153.
5.
DurduAKayabasiA (2024) Consensus-based virtual leader tracking algorithm for flight formation control of swarm UAVs. Turkish Journal of Electrical Engineering and Computer Sciences32(2): 251–267.
6.
HanZWangWHuangJ, et al. (2022) Distributed adaptive formation tracking control of mobile robots with event-triggered communication and denial-of-service attacks. IEEE Transactions on Industrial Electronics70(4): 4077–4087.
7.
HeSDongCDaiS, et al. (2022) Cooperative deterministic learning and formation control for underactuated USVs with prescribed performance. International Journal of Robust and Nonlinear Control32(5): 2902–2924.
8.
HuangYMengZDimarogonasDV (2024) Prescribed performance formation control for second-order multi-agent systems with connectivity and collision constraints. Automatica160: 111412.
9.
KongLReisJHeW, et al. (2024) Experimental validation of a robust prescribed performance nonlinear controller for an unmanned aerial vehicle with unknown mass. IEEE/ASME Transactions on Mechatronics29(1): 301–312.
10.
LevantA (2003) Higher-order sliding modes, differentiation and output-feedback control. International Journal of Control76(9): 924–941.
11.
LiBGongWYangY, et al. (2023) Distributed fixed-time leader-following formation control for multiquadrotors with prescribed performance and collision avoidance. IEEE Transactions on Aerospace and Electronic Systems59(5): 7281–7294.
12.
LiJDuJLLewisFL (2021) Distributed three-dimension time-varying formation control with prescribed performance for multiple underactuated autonomous underwater vehicles. International Journal of Robust and Nonlinear Control31(13): 6272–6287.
13.
LiuJYiPWangC (2022) Prescribed performance tracking for a class of uncertain nonlinear systems with actuator failures. International Journal of Robust and Nonlinear Control32(13): 7223–7239.
14.
LiuYHuangPZhangF, et al. (2020) Distributed formation control using artificial potentials and neural network for constrained multiagent systems. IEEE Transactions on Control Systems Technology28(2): 697–704.
15.
LiuYLiuXJingY (2018) Adaptive neural networks finite-time tracking control for nonstrict feedback systems via prescribed performance. Information Sciences468: 29–46.
16.
MinXBaldiSYuW (2023) Funnel-based asymptotic control of leader-follower nonholonomic robots subject to formation constraints. IEEE Transactions on Control of Network Systems10(3): 1313–1325.
17.
Olfati-SaberRFaxJAMurrayRM (2007) Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE95(1): 215–233.
18.
PengZWangDLiT, et al. (2020) Output-feedback cooperative formation maneuvering of autonomous surface vehicles with connectivity preservation and collision avoidance. IEEE Transactions on Cybernetics50(6): 2527–2535.
19.
RanjanPKSinhaACaoY, et al. (2024) Relational maneuvering of leader-follower unmanned aerial vehicles for flexible formation. IEEE Transactions on Cybernetics54(10): 5598–5609.
20.
ShiW (2021) Adaptive fuzzy output-feedback control for nonaffine MIMO nonlinear systems with prescribed performance. IEEE Transactions on Fuzzy Systems29(5): 1107–1120.
21.
ShiYYiBXieW, et al. (2024) Enhancing prescribed performance of tracking control using monotone tube boundaries. Automatica159: 111304.
22.
SongZWuZHuangH (2023) Cooperative learning formation control of multiple autonomous underwater vehicles with prescribed performance based on position estimation. Ocean Engineering280: 114635.
23.
TrejoJAVMedinaMABeltranCDG, et al. (2021) Robust observer-based leader-following consensus for multi-agent systems. IEEE Latin America Transactions19(11): 1949–1958.
24.
UllahNMehmoodYAslamJ, et al. (2021) UAVs-UGV leader follower formation using adaptive non-singular terminal super twisting sliding mode control. IEEE Access9: 74385–74405.
25.
WangXYeDZhangL, et al. (2024) Prescribed performance tracking control for nonlinear multiagent systems with distributed observation errors compensation. IEEE Transactions on Automation Science and Engineering22: 5182–5192.
26.
WangZZouYLiuY, et al. (2022) Distributed control algorithm for leader-follower formation tracking of multiple quadrotors: Theory and experiment. IEEE/ASME Transactions on Mechatronics26(2): 1095–1105.
27.
WuYWangZ (2020) Fuzzy adaptive practical fixed-time consensus for second-order nonlinear multiagent systems under actuator faults. IEEE Transactions on Cybernetics51(3): 1150–1162.
28.
YangSPanYCaoL, et al. (2024) Predefined-time fault-tolerant consensus tracking control for multi-UAV systems with prescribed performance and attitude constraints. IEEE Transactions on Aerospace and Electronic Systems60(4): 4058–4072.
29.
ZhangYChenJChenM, et al. (2024) Integrated the artificial potential field with the leader-follower approach for unmanned aerial vehicles cooperative obstacle avoidance. Mathematics12(7): 954.
30.
ZhangZDuanH (2024) Distributed velocity-free formation tracking control for clustered UAVs under virtual leader-follower framework. Science China Technological Sciences67: 1538–1552.
31.
ZhaoBXianBZhangY, et al. (2015) Nonlinear robust adaptive tracking control of a quadrotor UAV via immersion and invariance methodology. IEEE Transactions on Industrial Electronics62(5): 2891–2902.
32.
ZhouQLiHWangL (2018) Prescribed performance observer-based adaptive fuzzy control for nonstrict-feedback stochastic nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems48(10): 1747–1858.
