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气动参数在线辨识在火箭助推段制导中的应用 被引量:1

Application of aerodynamic parameter online identification in rocket ascent guidance
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摘要 为减小气动参数扰动对火箭助推段的影响,提高制导精度,本文将火箭飞行过程中的气动参数扰动量视为摄动制导中的小扰动量,对传统摄动方程进行改进。采用扩展Kalman滤波方法对气动参数扰动量进行辨识,再利用限定记忆递推最小二乘法基于辨识结果进行预测,将预测结果用于下一阶段的摄动制导计算,构建了“辨识—预测—计算”迭代更新的在线辨识模型。本文设计的扩展Kalman滤波器辨识效果较好,收敛时间10 s左右,辨识精度10%以内。设计的结合气动参数在线辨识的摄动制导方案相对于传统方案,能有效降低终端状态偏差。Monte Carlo打靶结果表明改进摄动制导方案对偏差的鲁棒性增强,弹道收敛性更好。本文将气动参数在线辨识用于火箭助推段的摄动制导,降低了气动参数扰动对制导精度的影响,在一定程度上提高了摄动制导的性能,对工程实践具有参考作用。 To decrease the impact of aerodynamic parameter disturbance on the rocket ascent stage and increase the accuracy of the ascent guidance,in this study,aerodynamic parameter disturbance is treated as a small perturbation in the process of perturbation guidance to improve the traditional perturbation equation.The extended Kalman filter(EKF)is applied to identify aerodynamic parameter disturbance,the fixed memory recursive least square method is used for predicting the value of the next moment based on the identification result,and the prediction result is used for the next stage′s guidance calculation.Furthermore,the identification-prediction-calculation periodically updated online identification model is built.The EKF designed in the study is proven to have good performance—the convergence time is about 10 s—and the identification accuracy is within 10%.The designed perturbation guidance combined with aerodynamic parameter online identification has significantly decreased the deviation of terminal state compared with the previous method.The results of a Monte Carlo simulation show that the improved perturbation guidance enhances the robustness to disturbance and improves the trajectory′s convergence.In this study,the aerodynamic parameter online identification approach is applied to the perturbation guidance of the rocket′s ascent stage.The proposed method can decrease the impact of aerodynamic parameter disturbance on guidance accuracy and improve the performance of conventional perturbation guidance to a certain degree,thus having reference significance for engineering practice.
作者 康继芝 张士峰 胡铖 KANG Jizhi;ZHANG Shifeng;HU Cheng(College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China)
出处 《哈尔滨工程大学学报》 EI CAS CSCD 北大核心 2020年第7期1052-1058,共7页 Journal of Harbin Engineering University
基金 国家自然科学基金项目(11804221)。
关键词 摄动制导 小扰动量 气动参数辨识 扩展卡尔曼滤波 限定记忆最小二乘法 perturbation guidance small perturbation aerodynamic parameter identification extended Kalman filter(EKF) fixed memory least square method
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