坦克炮控系统RBF神经网络自适应鲁棒控制方法研究

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (24) : 72-78.

论文

王一珉，杨国来，王丽群

作者信息 +

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

WANG Yimin，YANG Guolai，WANG Liqun

Author information +

文章历史 +

摘要

随着新一代坦克的发展，对其射击精度、炮口动能均提出了更高的要求，已有的坦克炮系统建模方法和控制策略已不能满足要求。为此，考虑车炮系统中非线性多因素，搭建了坦克行进间车炮系统多体动力学模型，并设计了基于RBF（radial basis function）神经网络补偿未建模扰动估计项的自适应鲁棒坦克炮控系统控制器。以行进间坦克的炮口扰动因素为优化目标，根据控制指令设计自适应律，实现未知系统参数的实时更新。仿真结果表明，新控制方法与传统的相比，具有更好跟踪效果和更强的鲁棒性。
关键词：车炮系统多体动力学；非线性；稳定系统；ARC；神经网络；机电一体化

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.

导出引用

王一珉，杨国来，王丽群. 坦克炮控系统RBF神经网络自适应鲁棒控制方法研究[J]. 振动与冲击, 2022, 41(24): 72-78

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

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