Adaptive robust control for a gun control system of a tank compensated by a RBF neural network

PDF(1588 KB)

Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (24) : 72-78.

WANG Yimin，YANG Guolai，WANG Liqun

Author information +

History +

Abstract

With the development of the new generation tank, higher requirements have been put forward for shooting accuracy and muzzle kinetic energy, which cannot be met by traditional tank system modeling methods and control strategies. Considering multiple nonlinear factors in the tank system, a multi-body dynamics model of the moving vehicle-gun system is established, and an adaptive robust tank controller based on RBF neural network compensation for unmodeled disturbance terms is designed. In this paper, the muzzle disturbance of the tank on the move is the optimization goal, and the adaptive law is designed according to the control command to realize the real-time update of unknown system parameters. The simulation results show that the controller designed in this research has better tracking effect and stronger robustness than traditional controllers.

Key words

multibody dynamics of the vehicle-gun system / nonlinear / stable system / ARC / neural network / mechatronics

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

WANG Yimin，YANG Guolai，WANG Liqun. Adaptive robust control for a gun control system of a tank compensated by a RBF neural network[J]. Journal of Vibration and Shock, 2022, 41(24): 72-78

References

[1] 马毓泽，杨国来. 坦克行进间垂向稳定器机电液系统的建模与仿真[J]. 弹道学报，2019, 31(01):23-28.
Ma Yu-ze, YANG Guo-lai. Modeling and Simulation of Mechanical Electro-hydraulic System of Vertical Stabilizer for a Moving Tank[J]. Journal of Ballistics, 2019, 31(01): 23-28.
[2] 郝丙飞，王红岩，芮强，等. 坦克多体系统动力学建模及模型试验验证[J]. 中国机械工程，2018, 29(04):429-433+440.
Hao Bing-fei, WANG Hong-yan, RUI Qiang, et al. Dynamics Modeling and Model Test Verification of Tank Multi-body Systems[J]. China Mechanical Engineering, 2018, 29(04): 429-433+440.
[3] 陈宇，杨国来，付羽翀，等. 高速机动条件下坦克行进间火炮非线性振动动力学研究[J]. 兵工学报，2019, 40(07):1339-1348.
Chen Yu, YANG Guo-lai, FU Yu-chong, et al. Dynamic Simulation on Nonlinear Vibration of Marching Tank Gun under High Mobility Conditions[J]. Acta Armamentarii, 2019, 40(07): 1339-1348.
[4] 周军，高跃飞，王登，等. 基于ADAMS的火炮发射动力学仿真研究[J]. 振动与冲击，2020, 39(23):135-140.
ZHOU Jun, GAO Yue-fei, WANG Deng, et al. Artillery launch dynamic simulation based on ADAMS[J]. Journal of Vibration and Shock, 2020, 39(23): 135-140.
[5] 王烨波，刘家辉，卫健，等. 机电伺服系统积分鲁棒自适应控制[J]. 科技创新与应用，2021, 11(23):7-11.
WANG Ye-bo, LIU Jia-hui, WEI Jian, et al. The Integral Robust Adaptive Control of Mechatronic Servo System[J]. Technology Innovation and Application, 2021, 11(23): 7-11.
[6] 郑颖. 某集束火箭炮位置伺服系统自抗扰方法研究[D]. 南京：南京理工大学，2015.
Zheng Ying. Active Disturbance Rejection Research on Position Servo System of a Certain Cluster Rocket Launcher[D]. Nanjing: Nanjing University of Science & Technology, 2015.
[7] 胡鑫，赵昕，李伟，等. 采用电动缸的火炮随动系统位置控制研究[J]. 兵器装备工程学报，2021, 42(01):245-248+258.
Hu Xin, ZHAO Xin, Li Wei, et al. Research on Position Control of Electric Cylinder Servo System[J]. Journal of Ordnance Equipment Engineering, 2021, 42(01): 245-248+258.
[8] 张博，周达，蒋波涛. 基于RBF神经网络和扰动观测器的PMLSM位置控制[J]. 组合机床与自动化加工技术，2021, (08):90-93.
ZHANG Bo, ZHOU Da, JIANG Bo-tao. Using RBF Neural Network and Disturbance-Observer to the Position Control of the PMLSM System[J]. Combined machine tool and automatic machining technology, 2021, (08): 90-93.
[9] 刘恒沙，管小荣，徐诚. 某车载火炮发射动力学参数灵敏度分析及优化[J]. 火力与指挥控制，2020, 45(01):22-26.
LIU Heng-sha, GUAN Xiao-rong, XU Cheng. Sensitivity Analysis and Optimization of Launching Dynamics Parameters of a Vehicle-mounted Artillery[J]. Fire Control & Command Control, 2020, 45(01):22-26.
[10] Ma Yuze, Yang Guolai, Sun Qinqin, et al. Adaptive robust control for tank stability: A constraint-following approach[J]. Proceedings of the Institution of Mechanical Engineers, 2020, 235(4): 095965182093784.
[11] Yu Chen, Guolai Yang, Quanzhao Sun. Dynamic simulation on vibration control of marching tank gun based on adaptive robust control[J]. Journal of Low Frequency Noise, Vibration and Active Control, 2020, 39: 146134841984668.
[12] 潘公宇. 汽车振动学基础及其应用[M]. 北京：北京大学出版社，2013.
[13] 陈宇，杨国来. 稳定器作用下坦克行进间炮口振动特性研究[J]. 振动与冲击，2019, 38(08):21-27.
CHEN Yu, YANG Guo-lai. A study on muzzle vibration characteristics of moving tanks under the influence of a stabilizer[J]. Journal of Vibration and Shock, 2019, 38(08): 21-27.
[14] 谢润. 车载武器行进间发射动力学研究[D]. 南京：南京理工大学, 2015.
XIE Run. Study on Launch Dynamics for Vehicle-Mounted Weapon on Traveling[D]. Nanjing: Nanjing University of Science & Technology, 2015.
[15] Kim S W. Evaluation of structural deformations of a mechanical connecting unit for oxidizer supplies by thermo-mechanical simulation [J]. Journal of Mechanical Science and Technology, 2016, 30(10): 4669-4677.
[16] 汪圆萍，张俊，张兴. 永磁同步电机位置伺服系统的RBF神经网络滑模控制策略[J]. 湖北大学学报(自然科学版)，2021, 43(04):429-436.
WANG Yuan-ping, ZHANG Jun, Zhang Xing. RBF neural network sliding mode control strategy for permanent magnet synchronous motor position servo system[J]. Journal of Hubei University (Natural Science), 2021, 43(04):429-436.
[17] 李程，廖丽诚，冯凌，等. 神经网络在永磁同步电机模型预测控制参数寻优中的应用[J]. 电源学报，2021, 19(04):86-94.
Li Cheng, LIAO Li-cheng, Feng Ling, et al. Application of Neural Network to Parameters Optimization of Permanent Magnet Synchronous Motor under Model Predictive Control[J]. Journal of Power Supply, 2021, 19(04):86-94.
[18] X Ma, W X Deng, Sh S Yuan, et al. Neural network based adaptive rise control of tank gun systems[J]. Journal of Physics: Conference Series, 2020, 1507: 052001.
[19] J Hu,S Cao,C Xu, et al. HIGH-ACCURACY MOTION CONTROL OF A MOTOR SERVO SYSTEM WITH DEAD-ZONE BASED ON A SINGLE HIDDEN LAYER NEURAL NETWORK[J]. Neural Network World, 2020, 30(1): 27-44.
[20] Feifei Liu, Xiaoting Rui, Hailong Yu,et al. Study on launch dynamics of the tank marching fire[J]. Journal of Shanghai Jiaotong University (Science), 2016, 21(4): 443-449.
[21] Qiang Gao, Zhan Sun, Guolai Yang,et al. A novel active disturbance rejection-based control strategy for a gun control system[J]. Journal of Mechanical Science and Technology, 2012, 26(12): 4141-4148.