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

被引：0
|
作者：
Wang Y. [1 ]
Yang G. [1 ]
Wang L. [1 ]
机构：
[1] School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2022年 / 41卷 / 24期
关键词：
adaptive robust control; mechatronics; multibody dynamics of the vehicle-gun system; neural network; nonlinear; stable system;
D O I：
10.13465/j.cnki.jvs.2022.24.009
中图分类号：
学科分类号：
摘要：
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 multibody dynamics model of the moving vehicle-gun system was established, and an adaptive robust tank controller based on RBF (radial basis function) neural network compensation for unmodeled disturbance terms was designed. In this paper, the muzzle disturbance of the tank on the move was the optimization goal, and the adaptive law was designed according to the control command to realize the real-time update of unknown system parameters. Simulation results show that the controller designed in this research has better tracking effect and stronger robustness than traditional controllers. © 2022 Chinese Vibration Engineering Society. All rights reserved.
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页码：72 / 78
页数：6
相关论文
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