Novel moderate transformation of fuzzy membership function into basic belief assignment

In information fusion, the uncertain information from different sources might be modeled with different theoretical frameworks. When one needs to fuse the uncertain information represented by different uncertainty theories, constructing the transformation between different frameworks is crucial. Various transformations of a Fuzzy Membership Function (FMF) into a Basic Belief Assignment (BBA) have been proposed, where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements. However, these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty. To avoid extreme attitudinal bias, a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred. In this paper, two moderate transformations of an FMF into a trade-off BBA are proposed. One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism, where the weighting factor can be user-specified or determined with some prior information. The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.     

永磁同步电机改进预测电流控制

在模型预测控制中,大量的计算会产生动作延迟,并且离散化方法的不准确性也会将误差引入系统,当采样时间较长时上述两种情况会导致系统性能恶化。为解决该问题,可采用双线性变换法获得更为精确的电机离散时间模型,该离散方法可保证系统有更优的稳定性。再通过引入延时补偿来解决预测电流时由于计算导致的动作延迟,从而减小电流纹波。基于此搭建仿真模型并进行试验研究,仿真与试验证明该预测系统具有动态跟踪特性好、电流纹波小和抗负载扰动强等优点。     

预设性能控制研究进展

介绍了预设性能控制的发展历程及研究现状,以具有一般形式的单输入单输出严格反馈非线性系统为例, 阐述了性能函数和误差变换的概念,分析了预设性能控制的基本原理。通过其应用实例说明,预设性能控制能够 解决工业生产、飞行器控制中的一些关键技术问题,因而有必要对预设性能控制方法进行深入研究。     

改进CST方法在翼型优化设计中的应用

形状类别函数变换(Class-Shape Transformation,CST)方法是近年来发展起来的一种新型气动外形参数化方法,该方法具有良好的鲁棒性,且涉及参数少、精度高,结果简单直观等特点,被广泛应用于翼型设计研究中。文章结合某小型无人机设计的工程实践,探讨了CST方法在小型无人机翼型设计中的应用,在借鉴他人研究成果的基础上,决定采用Bernstein多项式构建形状函数,分析了Bernstein多项式阶数对CST方法拟合精度的影响。仿真结果表明,当BPO4时,拟合精度能够达到满意的要求,可用于该型无人机翼型的设计与优化。     

STABC-IR: An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

The battlefield environment is changing rapidly, and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage. The current Intention Recognition (IR) method for air targets has shortcomings in temporality, interpretability and back-and-forth dependency of intentions. To address these problems, this paper designs a novel air target intention recognition method named STABC-IR, which is based on Bidirectional Gated Recurrent Unit (BiGRU) and Conditional Random Field (CRF) with Space-Time Attention mechanism (STA). First, the problem of intention recognition of air targets is described and analyzed in detail. Then, a temporal network based on BiGRU is constructed to achieve the temporal requirement. Subsequently, STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements. Finally, an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment. The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%, which is higher than other latest intention recognition methods. STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability, which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.