基于RBFNN和回馈递推的新型多旋翼飞行器控制

doi:10.3969/j.issn.0255-8297.2014.03.012

应用科学学报 ›› 2014, Vol. 32 ›› Issue (3): 301-310.doi: 10.3969/j.issn.0255-8297.2014.03.012

基于RBFNN和回馈递推的新型多旋翼飞行器控制

杨成顺1,2，杨忠1，葛乐1,2，黄宵宁2，张强3

1. 南京航空航天大学自动化学院，南京210016
2. 南京工程学院电力工程学院，南京211167
3. 济南大学自动化与电气工程学院，济南250022

收稿日期:2012-09-18 修回日期:2013-02-22 出版日期:2014-05-31 发布日期:2013-02-22
作者简介:杨成顺，博士，讲师，研究方向：飞行器故障诊断与容错控制，E-mail: yangchengshun@nuaa.edu.cn；杨忠，教授，博导，研究方向：先进飞行控制、机器人仿生，E-mail: yangzhong@nuaa.edu.cn
基金资助:
高等学校博士学科点专项科研基金(No.20113218110013)；江苏省普通高校研究生科研创新计划项目基金(No.CXLX110200)；
江苏省产学研联合创新基金(No.BY2012018)；山东省自然青年科学基金(No.ZR2012FQ030)资助

Control of a New Type Multi-rotor Aircraft with RBFNN and Backstepping

YANG Cheng-shun1,2, YANG Zhong1, GE Le1,2, HUANG Xiao-ning2, ZHANG Qiang3

1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2. School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
3. School of Electrical Engineering, University of Jinan, Jinan 250022, China

Received:2012-09-18 Revised:2013-02-22 Online:2014-05-31 Published:2013-02-22

摘要/Abstract

摘要： 研究了新型多旋翼飞行器的建模与轨迹跟踪控制. 建立了非线性运动学和动力学模型，并提出基于全调节径向基神经网络和回馈递推的鲁棒自适应轨迹跟踪控制策略. 首先设计了飞行器的位置误差PID控制器，用于实时消除飞行轨迹与期望轨迹的偏差，并为姿态控制环构建姿态角指令. 采用全调节径向基神经网络估计飞行器动力学模型中的复合干扰，为避免回馈递推控制器设计过程中对虚拟控制信号的繁琐求导运算，减小对解析模型的依赖度，设计了一种基于指令滤波回馈递推的飞行器姿态控制器. 该设计方法通过滤波器而非直接用解析方法对虚拟控制信号求导，大大简化了控制器的设计过程，节省了控制能量. 仿真实验表明所提出的轨迹跟踪策略的正确性和有效性.

关键词: 多旋翼飞行器, 鲁棒自适应, 神经网络, 指令滤波, 回馈递推

Abstract: Modeling and trajectory tracking control for a multi-rotor unmanned aerial vehicle (UAV) is studied. Nonlinear kinematics and dynamics models of the aircraft are established. A robust adaptive trajectory tracking control scheme based on fully tuned radial basis function neural network (FTRBFNN) and command
filtered backstepping is proposed for the multi-rotor aircraft. In the scheme, a position error PID controller of the aircraft is developed to eliminate deviation of the flight trajectory from the desired trajectory, and construct attitude angle commands for the attitude control loop. FTRBFNN is then used to estimate composite disturbance of the rotational dynamics. To avoid calculating pseudo control signal derivative analytically, and decrease dependence on the analytic model in the standard backstepping design, a command filtered backstepping technique is used to design the attitude controller. The technique uses a filter to calculate derivatives of the virtual control signal, instead of using analytical differentiation. It thus significantly simplifies
backstepping implementation and saves control energy. Correctness and effectiveness of the proposed robust adaptive trajectory tracking scheme are verified through simulation experiment.

Key words: multi-rotor aircraft, robust adaptive, neural network (NN), command filter, backstepping

中图分类号:

V249.122+.3

杨成顺1,2，杨忠1，葛乐1,2，黄宵宁2，张强3. 基于RBFNN和回馈递推的新型多旋翼飞行器控制[J]. 应用科学学报, 2014, 32(3): 301-310.

YANG Cheng-shun1,2, YANG Zhong1, GE Le1,2, HUANG Xiao-ning2, ZHANG Qiang3. Control of a New Type Multi-rotor Aircraft with RBFNN and Backstepping[J]. Journal of Applied Sciences, 2014, 32(3): 301-310.

参考文献

[1] 周红波，裴海龙，贺跃帮，孙太任. 状态受限的小型无人直升机轨迹跟踪控制[J]. 控制理论与应用，2012, 29(6): 778-784.

ZHOU Hongbo, PEI Hailong, HE Yuebang, SUN Tairen. Trajectory-tracking control for small unmanned helicopter with state constraints [J]. Control Theory & Applications, 2012, 29(6): 778-784. (in Chinese)

[2] 杨喜立，朱纪洪，黄兴李，胡春华. 倾转旋翼飞机建模与仿真[J]. 航空学报，2006, 24(4): 584 - 587.

YANG Xili, ZHU Jihong, HUANG Xingli, HU Chunhua. Modeling and simulation for tiltrotor airplane[J]. Acta Aeronautica et Astronatica Sinica, 2006, 24(4): 584 - 587. (in Chinese)

[3] ALTUG E, OSTROWSKI J P, TAYLOR C. J. Control of helicopter using dual camera visual feedback[J]. International Journal of Robotics Research, 2005, 24(5): 329 -341.

[4] MIAN A A, WANG D B. Modeling and backstepping-based nonlinear control strategy for a 6 DOF quadrotor helicopter[J]. Chinese Journal of Aeronautics, 2008, 21(3): 261-268.

[5] MU H, BIN X, CHEN D, YU F. Adaptive tracking control of under-actuated quadrotor unmanned aerial vehicles via backstepping[C]//Proceedings of the American Control Conference, New York: IEEE, 2010, 7: 2076-2081.

[6] BOUADI H, BOUCHOUCHA M, TADHINE M. Sliding mode control based on backstepping approach for an UAV type-quadrotor[J]. World Academy of Science, Engineering and Technology, 2008, 4(1): 12-17.

[7] ZUO Z. Trajectory tracking control design with command-filtered compensation for a quadrotor[J]. IET Control Theory and Application, 2010, 4(11): 2343- 2355.

[8] CHOI I H, BANG H C. Adaptive command filtered backstepping tracking controller design for quadrotor unmanned aerial vehicle[J]. Proceedings of the Institution of Mechanical Engineers, Part G: J. Aerospace Engineering, 2012, 226(5): 183-497.

[9] ERGINER B, ALTUG E.Design and implementation of a hybrid fuzzy logic controller for a quadrotor VTOL vehicle[J]. International Journal of Control, Automation, and Systems, 2012, 10(1): 61-70.

[10] 杨成顺，杨忠，许德智，葛乐. 新型六旋翼飞行器的轨迹跟踪控制[J]. 系统工程与电子技术，2012, 34(10): 2098-2105.

YANG Chengshun, YANG Zhong, XU Dezhi, GE Le. Trajectory tracking control for novel six-rotor aircraft[J]. Systems Engineering and Electronics[J], 2012, 34(10): 2098 -2105. (in Chinese)

[11] YANG Chengshun, YANG Zhong, HUANG X N, LI S B, ZHANG Q. Modeling and robust trajectory tracking control for a novel six-rotor unmanned aerial vehicle[J]. Mathematical Problems in Engineering, vol. 2013, Article ID 673525, 13 pages, 2013.

[12] 杨忠，黄宵宁，李桥梁．新型面对称布局多旋翼飞行器：中国， CN202071985U[P]. 2011-12-14.

YANG Zhong, HUANG Xiaoning, LI Qiaoliang A novel multi-rotor aircraft with symmetry layout[P]. Chinese Patent: CN202071985U，2011-12-14. (in Chinese)

[13] ZHOU Q L, ZHANG Y M, RABBATH C A, THEILLIEL D. Design of feedback linearization control and reconfigurable control allocation with application to a quadrotor UAV[C]//2010 Conference on Control and Fault Tolerant Systems, New York: IEEE, 2010: 371-376.

[14] DAS A, SUBBARAO K, LEWIS F. Dynamic inversion with zero-dynamic stabilization for quadrotor control[J]. IET Control Theory and Application, 2009, 3(3), 303-314.

[15] 陈勇，董新民，薛建平，王发威. 过驱动执行器故障自适应重构控制分配策略[J]. 应用科学学报，2011, 29(5): 537-544.

CHEN Yong, DONG Xinmin, XUE Jianping, WANG Fawei. Adaptive reconfigurable control allocation for over-actuated actuator failures[J]. Journal of Applied Sciences, 2011, 29(5): 537-544. (in Chinese)

[16] LEE D, KIM H J, SASTRY S. Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter[J]. International Journal of Control, Automation and System, 2009, 7(3), 419-428.

[17] 李光春，王璐，王兆龙，许德新. 基于四元数的四旋翼无人飞行器轨迹跟踪控制[J]. 应用科学学报，2012, 30(4): 415-422.

LI Gusngchun, WANG Lu, WANG Zhaolong, XU Dexin. Trajectory tracking control of quad-rotor UAV based on quaternion[J]. Journal of Applied Sciences, 2012, 30(4): 415-422. (in Chinese)

[18] 杨成顺，杨忠，黄宵宁，许德智. 四旋翼飞行器的分散式容错控制[J]. 应用科学学报，2013, 31(3): 321-330.

YANG Chengshun, YANG Zhong, HUANG Xiaoning, XU Dezhi. Distributed fault-tolerant control for quadrotor[J]. Journal of Applied Sciences, 2013, 31(3): 321-330. (in Chinese)

[19] DU Y L, WU Q X, JIANG C S. Robust optimal predictive control for a near space vehicle based on functional link network disturbance observer[J]. Journal of Astronautics, 2009, 30(4), 1489-1497.

[20] CHEN M, GE S Z S, HOW B V E. Robust Adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities[J]. IEEE Transactions on Neural Networks, 2010, 21(5):796- 812.

[21] XU Y F, JIANG B, TAO G, GAO Z F. Fault tolerant control for a class of nonlinear systems with application to near space vehicle[J]. Circuits Syst. Signal Process, 2011, 30(1): 655-672.

基于RBFNN和回馈递推的新型多旋翼飞行器控制

Control of a New Type Multi-rotor Aircraft with RBFNN and Backstepping

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