[1]JI Xiaoming,WEN Huaihai.Finite-time trajectory tracking control based on an adaptive neural network for a quadrotor UAV[J].CAAI Transactions on Intelligent Systems,2022,17(3):540-546.[doi:10.11992/tis.202104019]
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Finite-time trajectory tracking control based on an adaptive neural network for a quadrotor UAV

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 3 Page number: 540-546 Column: 学术论文—机器感知与模式识别 Public date: 2022-05-05
Title:
Finite-time trajectory tracking control based on an adaptive neural network for a quadrotor UAV
Author(s):
JI Xiaoming1;  WEN Huaihai2
1. Department of Electrical Engineering, Jiangsu College of Safety Technology, Xuzhou 221011, China;
2. School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
Keywords:
quadrotor aircraft; trajectory tracking; RBF neural network; adaptive law; global fast terminal sliding mode control; finite-time control; model uncertainty; external disturbance
CLC:
TP242
DOI:
10.11992/tis.202104019
Abstract:
Aimed at the trajectory tracking control problem of a quadrotor UAV with model uncertainties and unknown external disturbances, an adaptive global fast terminal sliding mode control method based on the RBF neural network has been proposed herein. The proposed method assists the system in tracking the desired trajectory in finite time. Considering the adaptability of global fast terminal sliding mode control in practical applications and chattering problems, the equivalent control quantity has been replaced by RBF neural networks. The chattering of the system has been reduced effectively by compensating for model uncertainty and unknown external disturbances with online learning of neural networks. According to the adaptive law derived from the Lyapunov method, the weights of neural networks are adjusted online to ensure the stability of the closed-loop system. Through a series of simulation examples and flight experiments, the effectiveness and feasibility of the proposed method have been validated. Results show that the proposed method has less chattering, better convergence, and anti-interference ability. It is also more robust toward model parameter perturbation compared to the sliding mode control.
References:
[1] 蒋林, 冷雪峰, 罗小华, 夏旭洪. 基于模糊单神经元PID的四旋翼控制研究[J]. 计算机仿真, 2019, 36(10): 39–43
JIANG LIN, LENG XUEFENG, LUO XIAOHUA, XIA XUHONG. Quadrotor control based on fuzzy-single neuron PID controller[J]. Computer simulation, 2019, 36(10): 39–43
[2] FOEHN P, ROMERO A, SCARAMUZZA D. Time-optimal planning for quadrotor waypoint flight[J]. Science robotics, 2021, 6(56): 1221–1240.
[3] MU C, ZHANG Y. Learning-based robust tracking control of quadrotor with time-varying and coupling uncertainties[J]. IEEE transactions on neural networks and learning systems, 2019, 31(1): 259–273.
[4] HOU Z, LU P, TU Z. Nonsingular terminal sliding mode control for a quadrotor UAV with a total rotor failure[J]. Aerospace science and technology, 2020, 98: 105716.
[5] ZHANG X, WANG Y, ZHU G, et al. Compound adaptive fuzzy quantized control for quadrotor and its experimental verification[J]. IEEE transactions on cybernetics, 2020, 51(3): 1121–1133.
[6] GUO K, JIA J, YU X, et al. Multiple observers based anti-disturbance control for a quadrotor UAV against payload and wind disturbances[J]. Control engineering practice, 2020, 102: 0967–0661.
[7] XIONG J J, ZHANG G. Discrete-time sliding mode control for a quadrotor UAV[J]. Optik, 2016, 127(8): 3718–3722.
[8] MOFID O, MOBAYEN S, WONG W K. Adaptive terminal sliding mode control for attitude and position tracking control of quadrotor UAVs in the existence of external disturbance[J]. IEEE access, 2020, 9: 3428–3440.
[9] YU Y, GUO Y, PAN X, et al. Robust backstepping tracking control of uncertain MIMO nonlinear systems with application to quadrotor UAVs[C]//2015 IEEE International Conference on Information and Automation. Lijiang, China, 2015: 2868–2873.
[10] GLIDA H E, ABDOU L, CHELIHI A, et al. Optimal model-free backstepping control for a quadrotor helicopter[J]. Nonlinear dynamics, 2020, 100(4): 3449–3468.
[11] BHATKHANDE P, HAVENS T C. Real time fuzzy controller for quadrotor stability control[C]//2014 IEEE International Conference on Fuzzy Systems. Beijing, China, 2014: 913–919.
[12] ALEXIS K, PAPACHRISTOS C, Siegwart R, et al. Robust model predictive flight control of unmanned rotorcrafts[J]. Journal of intelligent and robotic systems, 2016, 81(3-4): 443–469.
[13] 唐堂, 罗晓曙. 四旋翼无人机姿态非线性控制研究[J]. 计算机仿真, 2019, 36(1): 71–76
TANG TANG, LUO XIAOSHU. The research on nonlinear control of quadrotor UAV attitude[J]. Computer simulation, 2019, 36(1): 71–76
[14] AHMED N, CHEN M, SHAO S. Disturbance observer based tracking control of quadrotor with high-order disturbances[J]. IEEE access, 2020, 8: 8300–8313.
[15] HWANGBO J, SA I, Siegwart R, et al. Control of a quadrotor with reinforcement learning[J]. IEEE robotics and automation letters, 2017, 2(4): 2096–2103.
[16] SHARMA M, KAR I. Control of a quadrotor with network induced time delay[J]. ISA transactions, 2021, 111: 132–143.
[17] WANG N, DENG Q, XIE G, et al. Hybrid finite-time trajectory tracking control of a quadrotor[J]. ISA transactions, 2019, 90: 278–286.
[18] 刘凯悦, 冷建伟. 关于四旋翼无人机目标轨迹跟踪控制的研究[J]. 计算机仿真, 2017, 34(5): 103–107
LIU KAIYUE, LENG JIANWEI. The study on the desired trajectory tracking control of quadrotor UAV[J]. Computer simulation, 2017, 34(5): 103–107
[19] XIONG J J, ZHANG G B. Global fast dynamic terminal sliding mode control for a quadrotor UAV[J]. ISA transactions, 2017, 66: 233–240.
[20] HUA C C, WANG K, CHEN J N, et al. Tracking differentiator and extended state observer-based nonsingular fast terminal sliding mode attitude control for a quadrotor[J]. Nonlinear dynamics, 2018, 94(1): 343–354.
[21] ELIKER K, ZHANG W. Finite-time adaptive integral backstepping fast terminal sliding mode control application on quadrotor UAV[J]. International journal of control, automation and systems, 2020, 18(2): 415–430.
[22] JOKAR H, VATANKHAH R. Adaptive fuzzy global fast terminal sliding mode control of an over-actuated flying robot[J]. Journal of the brazilian society of mechanical sciences and engineering, 2020, 42(4): 1–18.
[23] CUI R, YANG C, LI Y, et al. Adaptive neural network control of AUVs with control input nonlinearities using reinforcement learning[J]. IEEE transactions on systems, man, and cybernetics:Systems, 2017, 47(6): 1019–1029.
[24] ZHU Y, FEI J. Adaptive global fast terminal sliding mode control of grid-connected photovoltaic system using fuzzy neural network approach[J]. IEEE access, 2017, 5: 9476–9484.
[25] LABBADI M, CHERKAOUI M. Robust adaptive nonsingular fast terminal sliding-mode tracking control for an uncertain quadrotor UAV subjected to disturbances[J]. ISA transactions, 2020, 99: 290–304.
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