摘要
火箭炮发射架位置伺服系统中存在燃气流冲击力矩、参数时变、外部扰动等非线性因素的作用,针对传统PID控制难以在此复杂工况下取得良好控制效果的缺点,文中设计了基于传统PID控制的RBF神经网络监督控制器,完成了神经网络离线样本的选取和训练算法的改进,利用神经网络自学习、自整定能力增强系统的自适应能力。仿真结果表明此复合控制策略可以有效提高系统的控制品质。
For existence of both disturbance of gas-fired impact and variation of parameters of rocket launcher,traditional PID controller may not meet high precision request,a novel supervised radial basis function neural network（RBF NN） controller based on PID control was designed.Off-line sample of NN was chosen and improvement of arithmetic was proposed.Adaptation of system was enhanced by abilities of self-learning and self-tuning of NN.Simulation shows this control strategy can improve the performance of rocket launcher servo system.
出处
《弹箭与制导学报》
CSCD
北大核心
2012年第5期179-182,共4页
Journal of Projectiles,Rockets,Missiles and Guidance
