基于RBF的舰载火箭炮最优滑模控制

RBF-Based Optimal Sliding Mode Control of Shipborne Artillery

针对舰载火箭炮在海上射击时的精度问题,提出一种基于RBF神经网络对最优滑模控制进行优化的控制策略。首先,建立舰载火箭炮随动系统数学模型,结合线性二次型最优控制理论与滑模控制设计一种全局最优滑模控制器(GROSMC),既提高了系统响应速度又保证了良好的鲁棒性;接着,采用RBF神经网络对切换控制项的增益进行动态调节,削弱滑模在切换时的抖振问题;最后,通过仿真对比验证所设计控制器的有效性,表明该策略具有良好的控制性能,满足系统要求。

To solve the problem of low firing accuracy of shipborne artillery at sea,an optimal sliding mode control strategy based on RBF neural network is proposed.Firstly,the mathematic model of shipborne artillery servo system is established,and a Global Robust Optimal Sliding Mode Controller(GROSMC)is designed based on the linear quadratic optimal control theory and sliding mode control.This improves the response speed and ensures the good robustness of the system.Then,RBF neural network is used to dynamically adjust the gain of the switching control item to reduce the sliding mode chattering problem.Finally,the effectiveness of the designed controller is verified by simulation comparison.The simulation results show that this strategy has good control performance and meets the system requirements.