考虑禁飞区的高超声速飞行器再入跟踪控制

为确立高超声速飞行器再入精确模型,参考NASA的研究数据对动力学方程中的气动参数进行拟合。基于飞行器结构的相关性质及战略应用背景,加入热流密度、动压、过载和禁飞区等参考约束的限制,融合成强非线性、复杂耦合的最优控制问题。采用改进自适应伪谱法求解模型所构建的微分方程组,通过设置自适应网格细化与配点,使单位时间内状态变量和控制变量波动过大的时间区间被进一步细划,并利用SNOPT求解器解算出符合条件的最优轨迹。设计闭环控制器,实现对最优轨迹的姿态变化的跟踪,测试系统的性能并进行评估。仿真结果表明:整个再入过程中,飞行器速度下降过程偏于平稳,再入轨迹可以满足约束条件,在避开禁飞区的同时取得最大横向航程;三通道角速度收敛可控,对姿态的跟踪较理想,控制器基本可以实现精确调姿。     

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics. Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing. Owing to the complexity of marine environment and the particularity of underwater acoustic channel, noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing. In order to solve the dilemma, we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), minimum mean square variance criterion (MMSVC) and least mean square adaptive filter (LMSAF). This noise reduction technique, named CEEMDAN-MMSVC-LMSAF, has three main advantages: (i) as an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN can better suppress mode mixing, and can avoid selecting the number of decomposition in variational mode decomposition (VMD); (ii) MMSVC can identify noisy intrinsic mode function (IMF), and can avoid selecting thresholds of different permutation entropies; (iii) for noise reduction of noisy IMFs, LMSAF overcomes the selection of decomposition number and basis function for wavelet noise reduction. Firstly, CEEMDAN decomposes the original signal into IMFs, which can be divided into noisy IMFs and real IMFs. Then, MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs. Finally, both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained. Compared with other noise reduction techniques, the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals, which has the better noise reduction effect and has practical application value. CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection, feature extraction, classification and recognition of underwater acoustic signals.     

基于正态分布曲线的分段式变步长LMS算法

针对传统最小均方误差(Least Mean Square, LMS)自适应滤波算法由于步长固定,在解决稳态误差与收敛性之间的关系时,始终处于矛盾状态的问题,在对传统的固定步长LMS自适应滤波算法分析的基础上,根据变步长LMS自适应滤波算法的步长调整原则,通过构造步长因子与误差信号的非线性函数,提出了一种基于正态分布曲线的分段式变步长LMS自适应滤波算法,并分析了参数取值对算法性能的影响。针对实际信号处理过程中参考信号难以选取的问题,提出了一种基于分裂阵的参考信号选取方法。理论和海试数据分析结果表明:该算法的收敛速度和稳态误差明显优于固定步长的LMS自适应滤波算法和基于Sigmoid函数的变步长LMS自适应滤波算法。