Optimal Control of Supercavitating Vehicles Based on Unscented Kalman Filter

doi:10.3969/j.issn.1000-1093.2019.06.014

Abstract

Abstract: An optimal control method based on unscented Kalman filter is proposed to solve the adverse effects due to environmental and measurement noises. Based on dynamic model of supercavitating vehicle, the environmental and measurement noises are added to the equation of state. An optimal controller without filter and with unscented Kalman filter was simulated and analyzed. Simulated results show that the tracking error of supercavitating vehicle is large，and its movement is extremely unstable under the disturbance of environmental and measurement noises. Under the action of the unscented Kalman filter, the tracking error of supercavitating vehicle is reduced significantly and the supercavitating vehicle reaches a full package state in a short period of time. It has better signal processing results. Key

Key words: supercavitatingvehicle, unscentedKalmanfilter, optimalcontrol, noise, track

CLC Number:

TJ630.1

ZHANG Chengju，WANG Cong， CAO Wei， WANG Jinqiang. Optimal Control of Supercavitating Vehicles Based on Unscented Kalman Filter[J]. Acta Armamentarii, 2019, 40(6): 1235-1243.

References

［1］ DZIEISKI J, KURDILA A. A benchmark control problem for supercavitating vehicles and an initial investigation of solutions [J]. Journal of Vibration and Control, 2003, 9(7): 791-804.
[2] VANEK B, BOKOR J, BALAS G J, et al. Longitudinal motion control of a high-speed supercavitation vehicle [J]. Journal of Vibration and Control, 2007, 13(2): 159-184.
[3] QIANG B C, HAN Y T, SUN Y, et al. Absolute stability control of supercavitating vehicles based on backstepping[C]∥Proceedings of the International Conference on Mechatronics and Automation. Tianjin, China: IEEE, 2014:1918-1923.
[4] NGUYEN V, BALACHANDRAN B. Supercavitating vehicles with noncylindrical, nonsymmetric cavities: dynamics and instabilities[J]. Journal of Computational & Nonlinear Dynamics, 2011, 6(4): 1396-1402.
[5] L R, YU K P, WEI Y J, et al. Adaptive robust controller for supercavitating vehicle using guaranteed cost theory[J].Journal of Harbin Institute of Technology, 2011, 18(4):77-81.
[6] RUZZENCE M, KAMADA R. Trajectory optimization strategies for supercavitating underwater vehicles [J]. Journal of Vibration and Control, 2008, 14(5): 611-644.
[7] MAO X F, WANG Q. Adaptive control design for a super-cavitating vehicle model based on fin force parameter estimation[J].Journal of Vibration and Control, 2013, 21(6):1220-1233.
[8] ZHAO X H, SUN Y，QI Z K, et al. Catastrop-he characteristics and control of pitching supercavitating vehicles at fixed depths[J].Ocean Engineering，2016，28（6）：185-194.
[9] 白涛，蒋运华，韩云涛. 基于混合扩展卡尔曼滤波的超空泡航行体变深运动控制研究 [J]. 兵工学报, 2017, 38(10):1980- 1987.
BAI T, JIANG Y H, HAN Y T. Research on variable-depth motion control of supercaviting vehicle based on hybrid extended Kalman filter [J]. Acta Armamentarii，2017,38(10):1980-1987. (in Chinese)

[10] 庞爱平，何朕，王京华，等. 超空泡H_∞航行体状态反馈设计[J].控制理论与应用，2018, 35(2):146-152.
PANG A P, HE Z, WANG J H, et al. H-infinty state feedback design for supercavitating vehicle [J]. Control Theory & Applications，2018, 35(2) :146-152. (in Chinese)

[11] LOGVINOVICH G V. Hydrodymics of flow with free boundaries [M]. Kiev,Ukraine: Naukova Dumka, 1969:128.
[12] 吕瑞，于开平，魏英杰, 等. 超空泡航行体的增益自适应变结构控制器设计 [J]. 兵工学报, 2010, 31(3):303-308.
L R， YU K P, WEI Y J, et al. Design of gain adaptive variable structure controller for supercavitating vehicle[J]. Acta Aemamentarii, 2010, 31(3):303-308. (in Chinese)

[13] 王京华，魏英杰，于开平, 等. 基于空泡记忆效应的水下超空泡航行体建模与控制[J]. 振动与冲击, 2010, 29(8):160-163.
WANG J H, WEI Y J, YU K P, et al. Modeling and control of underwater supercavitating vehicle based on memory effect of cavity[J]. Journal of Vibration and Shock.2010, 29(8):160-163. (in Chinese)
[14] 李洋，刘明雍，张小件, 等. 基于自适应RBF神经网络的超空泡航行体反演控制[J].自动化学报, 2018. ［2018-07-01］. http:∥kns.cnki.net/kcms/detail/11.2109.TP.20180418.1444.004.html.
LI Y, LIU M Y, ZHANG X J, et al.Adaptive RBF neural network based backstepping control for supercavitating vehicles.[J]. Acta Automatica Sinica, 2018. ［2018-07-01］. http:∥kns.cnki.net/kcms/detail/11.2109.TP.20180418.1444.004.html. (in Chinese)

[15] 陈超倩，曹伟，王聪, 等. 超空泡航行体最优控制建模与仿真[J]. 北京理工大学学报, 2016, 36(10):1031-1036.
CHEN C Q,CAO W, WANG C, et al.Modeling and simulating of supercavitating vehicles based on optimal control[J]. Transactions of Beijing Institute of Technology，2016,36(10):1031-1036. (in Chinese)

第40卷第6期
2019 年6月兵工学报ACTA
ARMAMENTARIIVol.40No.6Jun.2019

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