点击次数：
影响因子：5.0
DOI码：10.1016/j.ast.2023.108302
发表刊物：Aerospace Science and Technology
关键字：Tracking differentiator, Guidance law, Design optimization, Convolutional neural network
摘要：Tracking differentiator is widely used to estimate the differential signal of noisy systems with unknown structures. However, there still lacks an efficient approach to tracking differentiator design and optimization, partly due to the nonlinearity by nature, and the high dimension of design parameters. Thus, this paper proposes a learning-based design optimization method. First, neural networks are trained to approximate the multivariate influence on tracking differentiator performance. The resultant learning-based surrogate model is then used to optimize the parameters of tracking differentiator by differential evolution algorithm. The proposed method is applied to Sigmoid-type tracking differentiator and further compared to the unscented Kalman filter with various input signals. The estimation error of the optimized tracking differentiator is reduced by 65.70% compared to that of the benchmark method. The second-order tracking differentiator is integrated into an angular acceleration-based guidance law. Simulation results of tracking moving targets show that the miss distance and control resource are separately lowered by at least 35.70% and 74.12% as opposed to the crude guidance law.
论文类型：期刊论文
论文编号：108302
一级学科：航空宇航科学与技术
文献类型：期刊
卷号：137
页面范围：1-14
是否译文：否
发表时间：2023-03-31
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S1270963823001992