基于补偿函数观测器的四旋翼无人机姿态受限控制

doi:10.13229/j.cnki.jdxbgxb20220612

摘要/Abstract

摘要：

由于四旋翼无人机的飞行环境及自身执行机构等原因限制，为了保证姿态始终在约束范围内变化，引入障碍Lyapunov函数（BLF）保证系统能够达到预设性能。提出了一种基于补偿函数观测器（CFO）的反步姿态受限控制（CFO-BLF）方案。通过Simulink仿真对比了CFO-BLF反步控制、ESO-BLF反步控制和PID控制下四旋翼无人机的姿态跟踪性能，验证了 $本文$ CFO-BLF控制方法的有效性及优越性。

关键词: 控制理论与控制工程, 扩张观测器, 补偿函数观测器, 障碍Lyapunov函数, 反步控制

Abstract:

Due to the limitation of the flight environment of the quadrotor UAV and its own actuator， its attitude is often subject to various constraints. To ensure that the attitude always changes within the constraint range， the barrier Lyapunov function （BLF） was introduced to ensure that the attitude always varies within the constraint range to ensure that the system can achieve the preset performance. A compensation function observer based BLF attitude constrained backstepping control （CFO-BLF） scheme was proposed. Simulink simulation compares the CFO-BLF backstepping control， ESO-BLF backstepping control and PID control algorithms by testing the attitude tracking performance of the quadrotor UAV to verify the effectiveness and superiority of CFO-BLF.

Key words: control theory and control engineering, extend state observer （ESO）, compensation function observer （CFO）, barrier Lyapunov function, backstepping control

中图分类号:

TP273

齐国元,李阔,王琨. 基于补偿函数观测器的四旋翼无人机姿态受限控制[J]. 吉林大学学报(工学版), 2023, 53(3): 853-862.

Guo-yuan QI,Kuo LI,Kun WANG. Attitude constrained control of quadrotor unmanned aerial vehicle based on compensation function observer[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 853-862.

图/表 9

图1

图2

表1

观测器参数"

CFO	$λ$	8
	$l 1$	54
	$l 2$	432
ESO	$l 1$	24
	$l 2$	192
	$l 3$	512

表1

图3

图4

表2

控制器参数"

PID	$k P$	0.15
	$k I$	0.2
	$k D$	0.12
BLF	$τ 1$	500
	$τ 2$	0.01
	$γ ?$	0.15

表2

表3

模型参数"

变量	数值	变量	数值
$m / k g$	1.4	$k 1$	0.07
$g / (m ? s - 2)$	9.8	$k 2$	0.07
$l / m$	0.225	$k 3$	0.07
$I x x / (k g ? m 2)$	0.0211	$k 4$	0.03
$I y y / (k g ? m 2)$	0.0219	$k 5$	0.03
$I z z / (k g ? m 2)$	0.0366	$k 6$	0.03

表3

图5

图6

参考文献 32

1	Zhang Yong, Chen Zeng-qiang, Zhang Xing-hui, et al. A novel control scheme for quadrotor UAV based upon active disturbance rejection control[J]. Aerospace Science and Technology, 2018, 79: 601-609.
2	Hu Jun-yan, Niu Han-lin, Carrasco J, et al. Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring[J]. Aerospace Science and Technology, 2022, 123(2): No.107494.
3	Luis A B, Eduardo L C, Mario G S, et al. Towards a standard design model for quad-rotors: a review of current models, their accuracy and a novel simplified model[J]. Progress in Aerospace Sciences, 2017, 95: 1-23.
4	Liu Yang, Yao De-yin, Wang Li-jie, et al. Distributed adaptive fixed-time robust platoon control for fully heteroge-neous vehicles[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 53(1): 264-274.
5	Liu Yang, Li Hong-yi, Zuo Zong-yu, et al. An overview of finite/fixed-time control and its application in engineering systems[J]. IEEE/CAA Journal of Automatica Sinica, 2022, 9(12): 2106-2120.
6	Zhou Si-cheng, Guo Ke-xin, Yu Xiang, al et, Fixed-time observer based safety control for a quadrotor UAV[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(5): 2815-2825.
7	Kanellakopoulos I, Kokotovic P V. Systematic design of adaptive controllers for feedback linearizable systems[C]∥1991 American Control Conference, Boston, America, 1991: 1241-1253.
8	Simone M, Guida D. Control design for an under-actuated UAV model[J]. FME Transactions, 2018, 46(4): 443-452.
9	Sarwar S, Rehman S U. Mathematical modelling of unmanned aerial vehicles[J]. Mehran University Research Journal of Engineering and Technology, 2013, 32(4): 615-622.
10	Saidi E, Hammi Y, Douik A. Modelling and predictive control of an inverted pendulum system by MLD approach: multivariable case[J]. International Journal of Modelling, Identification and Control, 2017, 27(1): 40-48.
11	Masters S E, Challis J H. Increasing the stability of the spring loaded inverted pendulum model of running with a wobbling mass[J]. Journal of Biomechanics, 2021, 123: No.110527.
12	Labbadi M, Cherkaoui M. Robust adaptive backstepping fast terminal sliding mode controller for uncertain quadrotor UAV[J]. Aerospace Science and Technology, 2019, 93: No.105306.
13	Chen Fu-yang, Jiang Rong-qiang, Zhang Kang-kang, et al. Robust backstepping sliding-mode control and observer-based fault estimation for a quadrotor UAV[J]. IEEE Transactions on Industrial Electronics, 2016, 63(8): 5044-5056.
14	Basri M A M. Robust backstepping controller design with a fuzzy compensator for autonomous hovering quadrotor UAV[J]. Transactions of Electrical Engineering, 2018, 42(3): 379-391.
15	Swaroop D, Hedrick J K, Yip P P, et al. Dynamic surface control for a class of nonlinear systems[J]. IEEE Transactions on Automatic Control, 2000, 45(10): 1893-1899.
16	Fu Chun-yang, Hong Wei, Lu Hui-qiu, et al. Adaptive robust backstepping attitude control for a multi-rotor unmanned aerial vehicle with time-varying output constraints[J]. Aerospace Science and Technology, 2018, 78: 593-603.
17	Ding Chen, Ma Li, Ding Shi-hong. Second-order sliding mode controller design with mismatched term and time-varying output constraint[J]. Applied Mathematics and Computation, 2021, 407(2): No.126331.
18	Ngo K B, Mahony R, Jiang Z P. Integrator backstepping using barrier functions for systems with multiple state constraints[C]∥Proceedings of the 44th IEEE Conference on Decision and Control, Seville, Spain, 2005: 8306-8312.
19	Cui Lei, Hou Xiu-ying, Zuo Zhi-qiang, et al. An adaptive fast super-twisting disturbance observer-based dual closed-loop attitude control with fixed-time convergence for UAV[J]. Journal of the Franklin Institute, 2022, 359(6): 2514-2540.
20	Alattas K A, Mofid O, Alanazi A K, et al. Barrier function adaptive nonsingular terminal sliding mode control approach for quad-rotor unmanned aerial vehicles[J]. Sensors, 2022, 22(3): 909-909.
21	Dasgupta R, Roy S B, Bhasin S. Barrier-lyapunov function based dynamic surface control of quad-rotorcraft[J]. IFAC Papers OnLine, 2020, 53(2): 9378-9383.
22	Hou Zhong-sheng, Wang Zhuo. From model-based control to data-driven control: survey, classification and perspective[J]. Information Sciences, 2013, 235: 3-35.
23	Jiang Yu, Jiang Zhong-ping. Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics[J]. Automatica, 2012, 48(10): 2699-2704.
24	Ren Bei-bei, Zhong Qing-chang, Chen Jin-hao.Robust control for a class of nonaffine nonlinear systems based on the uncertainty and disturbance estimator[J]. IEEE Transactions on Industrial Electronics, 2015, 62(9): 5881-5888.
25	Zhong Qing-chang, Rees D. Control of uncertain LTI systems based on an uncertainty and disturbance estimator[J] Journal of Dynamic Systems, Measurement, and Control, 2004, 126(4): 905-910.
26	Sanz R, Garcia P, Zhong Qing-chang, et al. Control of disturbed systems with measurement delays: application to quadrotor vehicles[C]∥23rd Mediterranean Conference on Control and Automation, Torremolinos, Spain, 2015: 927-932.
27	周来宏, 窦景欣, 张居乾, 等. 基于改进反步法的四旋翼无人机轨迹跟踪控制[J]. 东北大学学报: 自然科学版, 2018, 39(1): 66-70.
	Zhou Lai-hong, Dou Jing-xin, Zhang Ju-qian, et al. Trajtecory tracking control for a quadrotor UAV based on improved backstepping[J]. Journal of Northeastern University (Nature Science), 2018, 39(1): 66-70.
28	鲜斌, 李杰奇, 古训.基于非线性扰动观测器的无人机地面效应补偿[J].吉林大学学报: 工学版, 2022, 52(8): 1926-1933.
	Xian Bin, Li Jie-qi, Gu Xun.Ground effects compensation for an unmanned aerial vehicle via nonlinear disturbance observer[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(8): 1926-1933.
29	Han Jing-qing. From PID to active disturbance rejection control[J]. IEEE Transactions on Industrial Electronics, 2009, 56(3): 900-906.
30	Qi Guo-yuan, Li Xia, Chen Zeng-qiang. Problems of extended state observer and proposal of compensation function observer for unknown model and application in UAV[J]. IEEE Transactions on Systems, Man and Cybernetics: Systems, 2022, 52(5): 2899-2910.
31	李霞, 齐国元, 郭曦彤, 等. 高阶微分反馈控制及在四旋翼飞行器中的应用[J]. 航空学报, 2022, 43(12): No.326047.
	Li Xia, Qi Guo-yuan, Guo Xi-tong, et al. High-order differential feedback control and its application in quadrotor[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(12): No.326047.
32	Beal T R. Digital simulation of atmospheric turbulence for dryden and von karman models[J]. Journal of Guidance, Control, and Dynamics, 1993, 16(1): 132-138.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

[1]	郭洪艳,于文雅,刘俊,戴启坤. 复杂场景智能车辆车道与速度一体化滚动优化决策[J]. 吉林大学学报(工学版), 2023, 53(3): 693-703.
[2]	王德军,张凯然,徐鹏,顾添骠,于文雅. 基于车辆执行驱动能力的复杂路况速度规划及控制[J]. 吉林大学学报(工学版), 2023, 53(3): 643-652.
[3]	徐卓君,王耀祥,黄兴,彭程. 多无人机地面移动目标搜寻和定位[J]. 吉林大学学报(工学版), 2023, 53(3): 832-840.
[4]	何德峰,周丹,罗捷. 跟随式车辆队列高效协同弦稳定预测控制[J]. 吉林大学学报(工学版), 2023, 53(3): 726-734.
[5]	齐国元,陈浩. 基于观测器的四旋翼控制-抗扰-避障一体化[J]. 吉林大学学报(工学版), 2023, 53(3): 810-822.
[6]	王宏志,王婷婷,胡黄水,鲁晓帆. 基于Q学习优化BP神经网络的BLDCM转速PID控制[J]. 吉林大学学报(工学版), 2021, 51(6): 2280-2286.
[7]	冯建鑫,王强,王雅雷,胥彪. 基于改进量子遗传算法的超声电机模糊PID控制[J]. 吉林大学学报(工学版), 2021, 51(6): 1990-1996.
[8]	马彦,黄健飞,赵海艳. 基于车间通信的车辆编队控制方法设计[J]. 吉林大学学报(工学版), 2020, 50(2): 711-718.
[9]	邓丽飞, 石要武, 朱兰香, 于丁力. SI发动机闭环系统故障检测[J]. 吉林大学学报(工学版), 2017, 47(2): 577-582.