基于气动参数调节的无人机抗扰动控制算法

doi:10.11896/jsjkx.190200371

摘要/Abstract

摘要： 无人机飞行受到气动阻尼扰动,从而导致控制稳定性不好。当前采用翼型截面气动参数调节的方法进行无人机抗扰控制,以扭角以及振动方向等参数为约束指标,参数调节的模糊度较大,对气动姿态参数调节的稳定性不好。文中提出基于气动参数调节的无人机抗扰动控制算法。该算法根据无人机的飞行工况构建各阶模态对应的气弹耦合方程,在速度坐标系、体坐标系、弹道坐标系三维坐标系下构建无人机的飞行动力学和运动学模型;采用卡尔曼滤波方法实现对无人机飞行参数的融合调节和小扰动抑制处理,并采用末端位置参考模型进行无人机飞行轨迹的空间规划设计;在卡尔曼滤波预估模型中实现对动力学模型的线性化处理,采用气弹模态参数识别方法进行无人机的飞行扰动调节;将姿态控制作为内环,获得位置环状态反馈调节参数;以无人机的升力系数和扭力系数作为气动惯性参数进行飞行姿态的稳定性调节,从而实现无人机抗扰动控制律的优化设计。采集飞机的俯仰角、横滚角和航向角作为原始数据在Matlab中进行仿真分析,仿真结果表明,采用所提方法进行无人机抗扰动控制的稳定性较好,对气动参数进行在线估计的准确性较高,航向角误差降低12.4%,抗扰动能力提升8dB,收敛时间比传统方法缩短0.14s,无人机飞行的抗扰动性和飞行稳定性得到提高。所提方法在无人机飞行控制中具有很好的应用价值。

关键词: 动参数调节, 动力学模型, 抗扰动控制, 无人机

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

中图分类号:

TP242

赵敏,戴凤智. 基于气动参数调节的无人机抗扰动控制算法[J]. 计算机科学, 2020, 47(3): 237-241. https://doi.org/10.11896/jsjkx.190200371

ZHAO Min,DAI Feng-zhi. Anti-disturbance Control Algorithm of UAV Based on Pneumatic Parameter Regulation[J]. Computer Science, 2020, 47(3): 237-241. https://doi.org/10.11896/jsjkx.190200371

参考文献

[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.

相关文章 15

[1]	蹇奇芮, 陈泽茂, 武晓康. 面向无人机通信的认证和密钥协商协议 Authentication and Key Agreement Protocol for UAV Communication 计算机科学, 2022, 49(8): 306-313. https://doi.org/10.11896/jsjkx.220200098
[2]	刘漳辉, 郑鸿强, 张建山, 陈哲毅. 多无人机使能移动边缘计算系统中的计算卸载与部署优化 Computation Offloading and Deployment Optimization in Multi-UAV-Enabled Mobile Edge Computing Systems 计算机科学, 2022, 49(6A): 619-627. https://doi.org/10.11896/jsjkx.210600165
[3]	陈博琛, 唐文兵, 黄鸿云, 丁佐华. 基于改进人工势场的未知障碍物无人机编队避障 Pop-up Obstacles Avoidance for UAV Formation Based on Improved Artificial Potential Field 计算机科学, 2022, 49(6A): 686-693. https://doi.org/10.11896/jsjkx.210500194
[4]	杜鸿毅, 杨华, 刘艳红, 杨鸿鹏. 基于网络媒体的非线性动力学信息传播模型 Nonlinear Dynamics Information Dissemination Model Based on Network Media 计算机科学, 2022, 49(6A): 280-284. https://doi.org/10.11896/jsjkx.210500043
[5]	谢万城, 李斌, 代玥玥. 空中智能反射面辅助边缘计算中基于PPO的任务卸载方案 PPO Based Task Offloading Scheme in Aerial Reconfigurable Intelligent Surface-assisted Edge Computing 计算机科学, 2022, 49(6): 3-11. https://doi.org/10.11896/jsjkx.220100249
[6]	史殿习, 刘聪, 佘馥江, 张拥军. GPS拒止环境下基于定位置信度的多无人机协同定位方法 Cooperation Localization Method Based on Location Confidence of Multi-UAV in GPS-deniedEnvironment 计算机科学, 2022, 49(4): 302-311. https://doi.org/10.11896/jsjkx.210200106
[7]	赵耿, 宋鑫宇, 马英杰. 混沌子载波调制的无人机安全数据链路 Secure Data Link of Unmanned Aerial Vehicle Based on Chaotic Sub-carrier Modulation 计算机科学, 2022, 49(3): 322-328. https://doi.org/10.11896/jsjkx.210200022
[8]	成昭炜, 沈航, 汪悦, 王敏, 白光伟. 基于深度强化学习的无人机辅助弹性视频多播机制 Deep Reinforcement Learning Based UAV Assisted SVC Video Multicast 计算机科学, 2021, 48(9): 271-277. https://doi.org/10.11896/jsjkx.201000078
[9]	徐浩, 刘岳镭. 基于深度学习的无人机声音识别算法 UAV Sound Recognition Algorithm Based on Deep Learning 计算机科学, 2021, 48(7): 225-232. https://doi.org/10.11896/jsjkx.200500091
[10]	孙一凡, 米志超, 王海, 赵宁. 基于分簇的拓扑自适应的无人机蜂群OLSR路由协议 Cluster-based Topology Adaptive OLSR Protocol for UAV Swarm Network 计算机科学, 2021, 48(6): 268-275. https://doi.org/10.11896/jsjkx.200500130
[11]	王宇晨, 齐文慧, 徐立臻. 基于区块链的无人机集群安全协作 Security Cooperation of UAV Swarm Based on Blockchain 计算机科学, 2021, 48(11A): 528-532. https://doi.org/10.11896/jsjkx.201100199
[12]	杨章林, 谢钧, 张耕强. 基于定向天线的飞行自组网定向路由协议综述 Review of Directional Routing Protocols for Flying Ad-Hoc Networks Based on Directional Antennas 计算机科学, 2021, 48(11): 334-344. https://doi.org/10.11896/jsjkx.210400182
[13]	赵晓薇, 朱小军, 韩周卿. 面向定位应用的无人机的悬停位置和飞行路径优化 Hover Location Selection and Flight Path Optimization for UAV for Localization Applications 计算机科学, 2021, 48(11): 345-355. https://doi.org/10.11896/jsjkx.201000105
[14]	游文静, 董超, 吴启晖. 大规模无人机自组网分层体系架构研究综述 Survey of Layered Architecture in Large-scale FANETs 计算机科学, 2020, 47(9): 226-231. https://doi.org/10.11896/jsjkx.190900164
[15]	赵学远, 周绍磊, 王帅磊, 闫实. 切换拓扑条件下的多无人机系统编队包含控制 Formation Containment Control of Multi-UAV System Under Switching Topology 计算机科学, 2020, 47(6A): 577-582. https://doi.org/10.11896/JsJkx.190700064

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed