This paper presents a distributed adaptive formation control method for uncertain multiple quadrotor systems under a directed graph that characterizes the interaction among the leader and followers. The proposed approach is based on an adaptive dynamic surface control, consensus algorithm and graph theory, where the system uncertainties are approximately modelled by interval type-2 fuzzy neural networks. The adaptive laws of interval type-2 fuzzy neural network parameters are derived from the stability analysis. In this study, the robust stability of the closed-loop system is guaranteed by the Lyapunov theorem, and the leader-follower formation goal can be asymptotically achieved. The developed control scheme is applied to the followers of quadrotor systems for performance evaluations. Simulation results are also provided to compare with the existing methods and reveal the superiority of the proposed adaptive formation controller.
BeardRWMcLainTWNelsonDBet al. (2006) Decentralized cooperative aerial surveillance using fixed-wing miniature UAVs. Proceedings of the IEEE94(7): 1306–1324.
2.
CabecinhasDCunhaRSilvestreC (2015) A globally stabilizing path following controller for rotorcraft with wind disturbance rejection. IEEE Transactions on Control Systems Technology23(2): 708–714.
3.
ChanWSChenCLChangYHet al. (2014) Formation control for uncertain multiple Euler-Lagrange systems with dynamic surface control and interval type-2 neuro-fuzzy networks. In: IEEE International Conference on Control and Automation 6870925, 18–20 June, pp. 231–236. Taichung, Taiwan Publisher: IEEE.
4.
ChangYHChanWS (2014) Adaptive dynamic surface control for uncertain nonlinear systems with interval type-2 fuzzy neural networks. IEEE Transactions on Cybernetics44(2): 293–304.
5.
ChangYHChangCWChenCLTaoCW (2012) Fuzzy sliding mode formation control for multi-robot systems: design and implementation. IEEE Transactions on Systems, Man and Cybernetics-Part B, 42(2): 444–457.
6.
ChangYHChanWSChangCW (2013) T-S fuzzy model based adaptive dynamic surface control for ball and beam system. IEEE Trans. on Industrial Electronics60(6): 2251–2263.
7.
ChavesANCugnascaPSNetoJJ (2014) Adaptive search control applied to search and rescue operations using unmanned aerial vehicles (UAVs). IEEE Latin America Transactions12(7): 1278–1283.
8.
ChoiYCAhnHS (2015) Nonlinear control of quadrotor for point tracking: Actual implementation and experimental tests. IEEE/ASME Trans. on Mechatronics20(3): 1179–1192.
9.
DongXYuBShiZZhongY (2015) Time-varying formation control for unmanned aerial vehicles: Theories and applications. IEEE Trans. On Control Systems Technology23(1): 340–348.
10.
FreddiALonghiSMonteriuA (2015) Nonlinear decentralized model predictive control for unmanned vehicles moving in formation. Information Technology and Control44(1): 89–97.
11.
Garcia-DelgadoLDzulASantibanezVLlamaM (2012) Quad-rotors formation based on potential functions with obstacle avoidance. IET Control Theory and Applications6(12): 1787–1802.
12.
GuerreroJACastilloPSalazarSLozanoR (2012) Mini rotorcraft flight formation control using bounded inputs. Journal of Intelligent & Robotic Systems65(1): 175–186.
13.
HanJXuYDiLChenYQ (2013) Low-cost multi-UAV technologies for contour mapping of nuclear radiation field. J. Intell. Robot. Syst. 70(14): 401–410.
14.
HanSLeeJM (2014) Partial tracking error constrained fuzzy dynamic surface control for a strict feedback nonlinear dynamic system. IEEE Transactions on Fuzzy Systems22(5): 1049–1061.
15.
JuangCFChenCY (2013) Data-driven interval type-2 neural fuzzy system with high learning accuracy and improved model interpretability. IEEE Trans. on Cybernetics43(6): 1781–1795.
16.
JuangCFHuangRBChengWY (2010) An interval type-2 fuzzyneural network with support-vector regression for noisy regression problems. IEEE Transactions on Fuzzy Systems18(4): 686–699.
17.
KhooSXieLManZ (2009) Robust finite-time consensus tracking algorithm for multirobot systems. IEEE/ASME Trans. on Mechatronics14(2): 219–228.
18.
KimCJChwaD (2015) Obstacle avoidance method for wheeled mobile robots using interval type-2 fuzzy neural network. IEEE Transactions on Fuzzy Systems23(3): 677–687.
19.
LiYTongSLiT (2015) Adaptive fuzzy output feedback dynamic surface control of interconnected nonlinear pure-feedback systems. IEEE Transactions on Cybernetics45(1): 138–149.
20.
LinYYChangJYPalNRLinCT (2013) A mutually recurrent interval type-2 neural fuzzy system (MRIT2NFS) with self-evolving structure and parameters. IEEE Transactions on Fuzzy Systems21(3): 492–509.
21.
LiuHWangXZhongY (2015) Quaternion-based robust attitude control for uncertain robotic quadrotors. IEEE Transactions on Industrial Informatics11(2): 406–415.
22.
MaJZhengZLiP (2015) Adaptive dynamic surface control of a class of nonlinear systems with unknown direction control gains and input saturation. IEEE Transactions on Cybernetics45(4): 728–741.
23.
RoldaoVCunhaRCabecinhasDSilvestreCOliveiraP (2014) A leader-following trajectory generator with application to quadrotor formation flight. Robotics and Autonomous Systems62(10): 1597–1609.
24.
RyanTKimHJ (2013) LMI-based gain synthesis for simple robust quadrotor control. IEEE Transactions on Automation Science and Engineering10(4): 1173–1178.
25.
ShangBWuCHuYYangJ (2014) An algorithm of visual reconnaissance path planning for UAVs in complex spaces. Journal of Computational Information Systems10(19): 8363–8370.
26.
ShenQJiangBCocquempotV (2014) Adaptive fuzzy observer-based active fault-tolerant dynamic surface control for a class of nonlinear systems with actuator faults. IEEE Transactions on Fuzzy Systems22(2): 338–349.
27.
SunGLiDRenX (2015) Modified neural dynamic surface approach to output feedback of MIMO nonlinear systems. IEEE Trans. on Neural Networks and Learning Systems26(2): 224–236.
28.
SwaroopDHedrickJKYipPPGerdesJC (2000) Dynamic surface control for a class of nonlinear systems. IEEE Transactions on Automatic Control45(10): 1893–1899.
29.
WangYWuQWangY (2014) Distributed cooperative control for multiple quadrotor systems via dynamic surface control. Nonlinear Dynamics75(3): 513–527.
30.
YuJShiPDongWet al. (2015) Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors. IEEE Transactions on Neural Networks and Learning Systems26(3): 640–645.
