Computer Science ›› 2020, Vol. 47 ›› Issue (3): 237-241.doi: 10.11896/jsjkx.190200371

• Artificial Intelligence • Previous Articles     Next Articles

Anti-disturbance Control Algorithm of UAV Based on Pneumatic Parameter Regulation

ZHAO Min,DAI Feng-zhi   

  1. (School of Electronic Information and Automation, Tianjin University of Science&Technology College, Tianjin 300202 China)
  • Received:2019-02-27 Online:2020-03-15 Published:2020-03-30
  • About author:ZHAO Min,born in 1992,master.Her main research interests include artificial control,information processing and Internet of things technology. DAI Feng-zhi,born in 1971,Ph.D,associate professor,master supervisor.His main research interests include control engineering.

Abstract: The control stability of UAV flight caused by aerodynamic damping disturbance is not good.At present,the aerodynamic parameter adjustment method of airfoil section is used to control UAV anti-disturbance,and the parameters such as torsion angle and vibration direction are taken as constraint index.The ambiguity of the parameter adjustment is large,and the stability of the pneumatic attitude parameter adjustment is not good.The anti-disturbance control algorithm of UAV based on aerodynamic parameter adjustment was proposed.According to the flight condition of UAV,the Aeroelastic coupling equations corresponding to each modal were constructed,in the velocity coordinate system and body coordinate system.The flight dynamics and kinematics model of UAV was constructed in the three-dimensional coordinate system of ballistic coordinate system.Kalman filtering method is used to realize the fusion adjustment of flight parameters and small disturbance suppression of UAV.The terminal position re-ference model is used to design the flight trajectory of UAV.The linearization of the dynamic model is realized in the Kalman filter prediction model,and the Aeroelastic modal parameter identification method is adopted.The attitude control is used as the inner loop to obtain the state feedback adjustment parameters of the position loop.The lift coefficient and torque coefficient of the UAV are used as the aerodynamic inertia parameters to adjust the stability of the flight attitude.The optimization design of anti-disturbance control law for UAV is realized.The pitch angle,roll angle and heading angle of the aircraft are collected and analyzed in Matlab as the original data.The simulation results show that the proposed method has a good stability in the anti-disturbance control of UAV.The accuracy of on-line estimation of aerodynamic parameters is high,the heading angle error is reduced by 12.4%,the anti-disturbance ability is improved by 8 dB,the convergence time is shortened by 0.14s,and the flight immunity and flight stability of UAV are improved.It has good application value in UAV flight control.

Key words: Dynamic parameter adjustment, UAV, Anti-disturbance control, Dynamic model

CLC Number: 

  • TP242
[1] ZHOU F,ZHENG W,WANG Z F.Real-time motion estimation for UAVs based on dissimilar multi-sensor data fusion[J].Robot,2015,37(1):94-101.
[2]BORISOV O I,GROMOV V S,PYRKIN A A,et al.Output robust control with anti-windup compensation for quadcopters[J].IFAC Papers OnLine,2016,49(13):287-292.
[3]LUO L,CHEN K,DU F P,et al.Surface fitting and position-pose measurements based on an improved SA-PSO algorithm[J].Journal of Tsinghua University (Science and Technology),2015,55(10):1061-1066.
[4]DI B,ZHOU R,DONG Z N.Cooperative localization and trac- king of multiple targets with the communication-aware unmanned aerial vehicle system[J].Control and Decision,2016,31(4):616-622.
[5]ALZU'BI H,MANSOUR I,RAWASHDEH O.Loon Copter:Implementation of a hybrid unmanned aquatic-aerial quadcopter with active buoyancy control[J].Journal of Field Robotics,2018,35(5):764-778.
[6]ZHAO Y Z,LIANG B W,BIAN H,et al.Design of Global Constant Balance Parallel Mechanism and Its Balance Performance Analysis[J].Journal of Mechanical Engineering,2019,55(1):25-31.
[7] LIU X F,QIU L,LUAN X L,et al.Data Synchronization and Recursive Optimization of Uneven-length Batch Processes[J].Information and Control,2018,47(4):448-454.
[8]CHEN G R,WANG J Z,WANG S K,et al.Application of a new adaptive robust controller design method to electro-hydraulic servo system[J].Acta Automatica Sinica,2016,42(3):375-384.
[9]GAO J,PROCTOR A,ALISON P,et al.Sliding mode adaptive neural network control for hybrid visual servoing of underwater vehicles[J].Oceans,2017,142(7):666-675.
[10]OFODILE N A,TURNER M C.Anti-windup design for input-coupled double integrator systems with application to quadrotor UAV’s[J].European Journal of Control,2017,38:22-31.
[11]ZHONG D J,FENG X,YU H Q.Migration optimization algorithm based on state transition and fuzzy thinking [J].Computer Science,2019,46(1):112-116.
[12] CUI C,DENG Z H,WANG S T.Radial Basis Function Neural Network Model Based on Lasso Sparse Learning[J].Computer Engineering,2019,45(2):173-177.
[13]HELMY A,HEDAYAT A,AL-DHAHIR N.Robust weighted sum-rate maximization for the multi-stream MIMO interference channel with sparse equalization[J].IEEE Transactions on Communications,2015,60(10):3645-3659.
[14]HANSEN T L,BADIU M,FLEURY B H,et al.A sparse Bayesian learning algorithm with dictionary parameter estimation[C]∥Sensor Array and Multichannel Signal Processing Workshop.2014:385-388.
[15]MOHEBBI A,KESHMIRI M,XIE W.A comparative studyof eye-in-hand image-based visual servoing:Stereo vs.mono[J].Journal of Integrated Design and Process Science,2015,19(3):25-54. DAI W R,WANG J H.Research on Evaluation of UAV Vertical Gyroscope and Its Link Health Status.Journal of Chongqing University of Technology(Natural Science),2017, 31(9):138-144.
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[2] WU Zhong-zhi, WANG Lei, MA Jian-ping, TAN Si-yang, GUO Man-yi. Design of Typical Quadrotor UAV Based on System Architecture [J]. Computer Science, 2019, 46(11A): 575-579.
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[4] CHEN Jin-yin, HU Ke-ke,LI Yu-wei. Research on UAV Multi-point Navigation Algorithm Based on MB-RRT* [J]. Computer Science, 2018, 45(6A): 85-90.
[5] WU Xiao-yan, HUANG Jia-qi and BU Xiang-wei. Adaptive Backstepping Controler of Quadrotor UAV [J]. Computer Science, 2017, 44(Z6): 526-528, 538.
[6] CHEN Jin-yin, LI Yu-wei and DU Wen-yao. UAV Navigation Algorithm Research Based on EB-RRT* [J]. Computer Science, 2017, 44(Z11): 72-79.
[7] CHEN Jin-yin, SHI Jin, DU Wen-yao and WU Yang-yang. MB-RRT* Based Navigation Planning Algorithm for UAV [J]. Computer Science, 2017, 44(8): 198-206.
[8] REN Wei-jian, WANG Zi-wei and KANG Chao-hai. Remote Sensing Image of UAV Registration Based on Improved SIFT Algorithm [J]. Computer Science, 2015, 42(Z11): 179-182.
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[11] PAN Ming-xing and SUN Han. Fast and Efficient Algorithm for Airborne Target Recognition [J]. Computer Science, 2014, 41(Z6): 150-152.
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[14] . Decentralized Multi-Agent Based Cooperative Path Planning for Multi-UAVs [J]. Computer Science, 2012, 39(1): 219-222,233.
[15] ZHOU Wei,WEI Rui-xuan. Design and Implement of UAVs Testbed Based on 3D Environment [J]. Computer Science, 2010, 37(4): 258-.
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[10] LI Jian-fei, XU Kai-yong and JIN Lei. Trustworthy Software Distributing Mode Based on Software Description[J]. Computer Science, 2015, 42(12): 224 -228, 262 .