基于MPC的无人机航迹跟踪控制器设计

doi:10.3969/j.issn.1001-506X.2021.01.23

摘要/Abstract

摘要：

针对固定翼无人机航迹跟踪问题,采用基于状态扩展的双反馈模型预测控制理论对控制器进行设计。首先推导基于侧向偏差的无人机侧向航迹跟踪模型,采用动态逆方法对模型进行线性化处理,在此基础上设计基于状态扩展的双反馈模型预测控制器,并采用量子粒子群优化(quantum particle swarm optimization, QPSO)算法对控制器参数进行优化,考虑无人机飞行过程中受到的未知干扰,引入扩张状态观测器(extended states observer, ESO)对干扰进行观测,进一步提高系统的鲁棒性,并结合实际工程应用对系统进行数学仿真。仿真结果表明,基于状态扩展双反馈模型预测控制的无人机侧向航迹跟踪控制器,能够在系统存在模型不确定性与受到动态干扰时对期望航迹进行准确、稳定的跟踪。

关键词: 模型预测控制, 状态扩展, 航迹跟踪, 量子粒子群优化, 扩张状态观测器, 动态逆

Abstract:

Aiming at the problem of trajectory tracking of fixed-wing unmanned aerial vehicle, the controller is designed by using the dual-feedback model predictive control theory based on state expansion. Firstly, the unmanned aerial vehicle side trajectory tracking model based on the lateral deviation is derived, and the dynamic inverse method is used to linearize the model. Based on this, a dual feedback model predictive controller based on state expansion is designed, and the parameters of the controller are optimized using quantum particle swarm optimization (QPSO). Then, considering the unknown interference encountered during the flight, the extended states observer (ESO) is introduced to observe the interference, which further improves the robustness of the system. Finally, the system is mathematically simulated in combination with actual engineering applications. Simulation results show that the side trajectory tracking controller of unmanned aerial vehicle based on the state expansion dual feedback model predictive control can accurately and stably track the expected trajectory when the system has model uncertainty and is subject to dynamic interference.

Key words: model predictive control (MPC), state expansion, trajectory tracking, quantum particle swarm optimization (QPSO), extended states observer (ESO), dynamic inverse

中图分类号:

V279

王晓海, 孟秀云, 李传旭. 基于MPC的无人机航迹跟踪控制器设计[J]. 系统工程与电子技术, 2021, 43(1): 191-198.

Xiaohai WANG, Xiuyun MENG, Chuanxu LI. Design of trajectory tracking controller for UAV based on MPC[J]. Systems Engineering and Electronics, 2021, 43(1): 191-198.

图/表 7

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参考文献 25

1	LI C Y , JING W X . Application of PID controller to 2D diffe-rential geometric guidance problem[J]. Journal of Control Theory and Applications, 2007, 5 (3): 285- 290.
2	李樾, 陈清阳, 侯中喜. 自适应引导长度的无人机航迹跟踪方法[J]. 北京航空航天大学学报, 2017, 43 (7): 1481- 1490.
	LI Y , CHEN Q Y , HOU Z X . Path following method with adaptive guidance length for unmanned aerial vehicles[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43 (7): 1481- 1490.
3	NELSON D R , BARBER D B , MCLAIN T W , et al. Vector field path following for miniature air vehicles[J]. IEEE Trans.on Robotics, 2007, 23 (3): 519- 520.
4	LAWRENC D A , FREW E W , PISANO W J . Lyapunov vector fields for autonomous UAV flight control[J]. Journal of Gui-dance Control and Dynamics, 2008, 31 (5): 1220- 1229.
5	KOTHRAI M , POSTLETHWAIT I , GU D W . UAV path following in windy urban environments[J]. Journal of Intelligent & Robotic Systems, 2014, 74 (3/4): 1013- 1028.
6	钱积新. 预测控制[M]. 北京: 化学工业出版社, 2007.
	QIAN J X . Predictive control[M]. Beijing: Chemical Industry Press, 2007.
7	WANG P , TANG G J , LIU L H , et al. Nonlinear hierarchy-structured predictive control design for a generic hypersonic vehicle[J]. Science China Technological Sciences, 2013, 56 (8): 2025- 2036.
8	KOO S , KIM S , SUK J , et al. Improvement of shipboard land-ing performance of fixed-wing UAV using model predictive control[J]. International Journal of Control, Automation and Systems, 2018, 16 (6): 2697- 2708.
9	KOO S , KIM S , SUK J , et al. Model predictive control for UAV automatic landing on moving carrier deck with heave motion[J]. International Federation of Automatic Control PapersOnLine, 2015, 48 (5): 59- 64.
10	BACA T , STEPAN P , SPURNY V , et al. Autonomous landing on a moving vehicle with an unmanned aerial vehicle[J]. Journal of Field Robotics, 2019, 36 (5): 874- 891.
11	钱杏芳, 林瑞雄, 赵亚男. 导弹飞行力学[M]. 北京: 北京理工大学出版社, 2012.
	QIAN X F , LIN R X , ZHAO Y N . Missile flight mechanics[M]. Beijing: Beijing Institute of Technology Press, 2012.
12	NORDSJ A E . Cramer-Rao bounds for a class of systems described by Wiener and Hammerstein models[J]. International Journal of Control, 1997, 68 (5): 1067- 1083.
13	WANG W , HENRIKSEN R . Generalized predictive control of nonlinear systems of the Hammerstein form[J]. Modeling, Identification and Control, 1994, 15 (4): 253- 262.
14	SZNAIER M . Computational complexity analysis of set membership identification of Hammerstein and Wiener systems[J]. Automatica, 2009, 45 (3): 701- 705.
15	LAWRYNCZUK M . Nonlinear predictive control for Hammerstein-Wiener systems[J]. ISA Transactions, 2015, 55, 49- 62.
16	DOYLE F J , OGUNNAIKE B A , PEARSON R K . Nonlinear model-based control using second-order Volterra models[J]. Automatica, 1995, 31 (5): 697- 714.
17	ZHENG Q G , WANG Y , SUN F Y , et al. Aero-engine direct thrust control with nonlinear model predictive control based on linearized deep neural network predictor[J]. Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, 2020, 234 (3): 330- 337.
18	WU Z , RINCON D , CHRISTOFIDES P D , et al. Process structure-based recurrent neural network modeling for model predictive control of nonlinear processes[J]. Journal of Process Control, 2020, 89, 74- 84.
19	PATAN K . Two stage neural network modelling for robust model predictive control[J]. ISA Transactions, 2018, 72, 56- 65.
20	TENG L , WANG Y Y , CAI W J , et al. Fuzzy model predictive control of discrete-time systems with time-varying delay and disturbances[J]. IEEE Trans.on Fuzzy Systems, 2018, 26 (3): 1192- 1206.
21	LEE D , HU J H . Local model predictive control for T-S fuzzy systems[J]. IEEE Trans.on Cybernetics, 2017, 47 (9): 2556- 2567.
22	秦赟.基于改进粒子群算法的无人机航迹规划[D].成都:电子科技大学, 2011.
	QIN Y. UAV path planning based on improved particle swarm optimization[D]. Chengdu: University of Electronic Science and Technology of China, 2011.
23	PARSOPOULOS K E , VRAHATIS M N . Recent approaches to global optimization problems through particle swarm optimization[J]. Natural Computing, 2002, 1 (2): 235- 306.
24	邹丹丹, 沈炯, 张俊礼. 基于ITAE评价的无自平衡对象控制系统解析整定[J]. 工业控制计算机, 2018, 31 (7): 39- 40, 42.
	ZOU D D , SHEN J , ZHANG J L . ITAE index and analytical tuning methods for integral thermal process[J]. Industrial Control Computer, 2018, 31 (7): 39- 40, 42.
25	GAO Z Q. Scaling and bandwidth-parameterization based controller tuning[C]//Proc.of the American Control Conference, 2003: 4989-4996.

未标准化			标准化
f_n1	f_n2	f_n1/f_n2	f₁	f₂	f₁/f₂
1 835.04	50.33	36.46	0.61	0.17	3.59
912.08	63.39	14.39	0.30	0.21	1.43
1 347.76	88.58	15.22	0.45	0.30	1.50

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