Recursive filtering for state‐saturated systems with randomly occurring nonlinearities and missing measurements

This paper deals with the filtering problem for a class of discrete‐time state‐saturated systems subject to randomly occurring nonlinearities and missing measurements. A set of mutually independent Bernoulli random variables is used to describe the random occurrence of the missing measurements. Due to the simultaneous consideration of the state saturation, the randomly occurring nonlinearities, and the missing measurements, it is extremely hard to calculate the actual filtering error covariance in a closed form. As such, the objective of this paper is to construct an upper bound for the filtering error covariance and then design the filter parameters to minimize such an upper bound. The performance of the proposed filters is analyzed in terms of boundedness and monotonicity. Specially, we have shown that the minimum upper bound is always bounded under a mild assumption. Moreover, the relationship between the estimator performance and the arrival probability of the measurements is discussed. A numerical simulation is used to demonstrate the effectiveness of the filtering method.     

大数据技术课程混合式教学探索与实践

针对应用型本科高校学生大数据技术能力培养面临的主要问题,本文研究了大数据技术课程的线上线下混合式教学方法,探索了“线上自主学、线下课堂学、引入结对实验、过程化考核”的混合式教学模式,阐述了该模式的具体实施方案,取得了一定的成效,为大数据技术类课程建设提供一种可行的建设思路。     

A tunnel lighting control system based on human vision and variable reduction coefficient

Due to the changeable brightness outside the tunnel, the lighting brightness at the exit and entrance of the tunnel needs to be adjusted in time, so as to ensure that the driver can feel the difference of light intensity within the range that the human eye can adapt to when passing through the tunnel. In this paper, a tunnel lighting control strategy based on variable reduction factor is proposed, which can ensure that the lighting around the tunnel is within the range that the human eye can adapt to at any time. Through the field data collection and comparison and experiments, it can be shown that the lighting control method not only can improve the lighting quality in the tunnel, but also have better energy-saving effect and can reduce the cost of manual operation and maintenance.     

基于动态区域剔除的RGB-D视觉SLAM算法

针对动态场景下视觉SLAM(simultaneous localization and mapping)算法易受运动特征点影响,从而导致位姿估计准确度低、鲁棒性差的问题,提出了一种基于动态区域剔除的RGB-D视觉SLAM算法。首先借助语义信息,识别出属于移动对象的特征点,并借助相机的深度信息利用多视图几何检测特征点在此时是否保持静止;然后使用从静态对象提取的特征点和从可移动对象导出的静态特征点来微调相机姿态估计,以此实现系统在动态场景中准确而鲁棒的运行;最后利用TUM数据集中的动态室内场景进行了实验验证。实验表明,在室内动态环境中,所提算法能够有效提高相机的位姿估计精度,实现动态环境中的地图更新,在提升系统鲁棒性的同时也提高了地图构建的准确性。     

A deep feature alignment adaptation network for rolling bearing intelligent fault diagnosis

Fault diagnostic methods based on deep learning achieve impressive progress recently, but most studies assume that signals from the source domain and target domain share a similar probability distribution. However, the domain shift phenomenon is often unavoidable in practical engineering because of changeable conditions, which hinders the performance of some intelligent methods in fault diagnosis. To tackle the above issue, an unsupervised domain adaptation approach called Deep Feature Alignment Adaptation Network (DFAAN) is proposed in this paper to raise the domain adaptability of fault diagnosis. Firstly, the latent distributions of the two domains are aligned indirectly guided by a Gaussian prior to create a common latent space, which can promote the feature alignment across different domains. Secondly, to better narrow the discrepancy of the feature distribution with the Gaussian prior, a novel discriminative reconstruction distance based on the mechanism of the autoencoder is introduced. Thirdly, an entropy minimum technique is incorporated in the objective function to further increase the transferability of the adaptation method. Diagnostic experiments are conducted on two bearing datasets to illustrate the effectiveness of the proposed approach. The results reveal the superiority of the proposed approach over other typical methods and validate the versatility in multiple diagnostic tasks.     

随机激励下非线性水轮机系统的控制设计

针对一类随机非线性哈密顿系统提出了一种全新的反馈跟踪控制方法.该控制策略可以准确地控制系统输出的概率密度分布特性.闭环系统的稳定性也通过李雅普诺夫函数法得到严格的数学证明.最后,以随机非线性水轮机系统为例,详细演示了控制设计过程及其有效性.仿真结果表明,新的反馈控制策略可以使水轮机系统的输出满足预先指定的平稳概率密度函数.     

形状记忆合金的应用

作为一种新型的材料,形状记忆合金在各领域得到了广泛的应用。因其功能特性,可以用于机械、环保;而由于智能特性,正逐渐被广泛地应用于医学和电子领域。主要介绍了形状记忆合金在机械、环保、医学和电子方面的应用。     

基于Ansoft的电力机器人永磁吸附单元有限元分析

为满足爬壁机器人在狭窄的发电机定子气隙中能可靠地运行,设计了一种基于Halbach阵列的永磁吸附单元。使用Ansoft Maxwell软件建立二维磁场模型,采用有限元分析法对新型吸附单元的磁场进行分析,得到了其磁力线与磁感应强度的分布;通过与传统永磁吸附单元磁场的比较,得出新型吸附单元具有永磁磁能利用率高、单边磁场的特点,验证了新型吸附单元在发电机定子气隙中吸附的可靠性;并分析了结构参数的变化对吸附性能的影响,为吸附单元结构设计提供了参考数据。     

无电压限幅环节的无传感器永磁同步电机矢量控制技术

在电机高速弱磁或过载要求高的情况下,需要通过脉冲宽度调制(PWM)的过调制运行提高直流母线电压利用率。基于磁链观测的无传感器永磁同步电机矢量控制(磁场导向控制,FOC)技术具有算法简单、易实现等优点,但在过调制的情况下电压限幅环节会引起磁链观测及转子位置角估算误差。为提高PWM过调制情况下系统的控制性能,对传统的FOC控制框图进行了改进,省去了电压限幅环节并利用由占空比计算的实际电压对定子磁链进行估算。MATLAB仿真验证了改进方法的有效性。研究结果表明:该方法充分利用PWM调制自有的限幅特性,既能简化控制算法又能提高磁链观测精度,具有较好的技术经济性能。     

雷达伺服系统耦合力矩对跟踪精度影响分析

针对载机机动运动导致雷达伺服机构中存在复杂的耦合扰动力矩,无法实现伺服控制中扰动力矩精确补偿的问题,展开雷达伺服系统耦合力矩的建模和影响分析。采用矢量叠加原理推导两维机构的运动学方程和惯量耦合方程,建立方位和俯仰两轴的动力学耦合力矩模型,并通过仿真分析载机机动和不机动情况下,载机平台对两维框架的动力学耦合力矩和位置跟踪误差的影响。仿真结果表明,载机不机动时两维框架间的耦合力矩较小,跟踪误差较小;当载机机动时两维框架间耦合力矩增大,且耦合力矩表现为时变的正弦特性,此时跟踪误差增大且表现为时变和不规则的特点。动力学耦合力矩模型的建立为机载雷达伺服控制系统的设计提供了理论依据。