考虑前馈作用的BTT导弹自动驾驶仪设计方法研究

针对BTT导弹自动驾驶仪的设计要求,文章提出了一种考虑前馈作用的导弹自动驾驶仪控制器设计方法。基于该型导弹三通道弹体数学模型,建立了自动驾驶仪控制系统数学模型。应用增益技术和递推设计方法,并基于过载稳定跟踪控制的设计要求,提出了一种全局非线性稳定控制策略,设计了连续的非线性自动驾驶仪,解决了该导弹俯仰通道的前馈问题;对于偏航通道,通过设计三通道协调控制律,将有前馈的自动驾驶仪设计问题转化为无前馈的设计问题;同时也利用滑模变结构控制原理解算出了横滚通道的舵偏角指令。数字仿真结果表明,所设计的自动驾驶仪控制系统可有效克服前馈问题所产生的稳态误差以及不确定性因素的影响,同时也提高了跟踪精度,并具有良好的动态特性和稳态品质。     

基于物联网技术的锂电池故障在线测量和远程诊断

锂电池作为新型绿色能源越来越多地使用在电动自行车、电动汽车中。锂动力电池管理问题也随之而来,主要难题包括锂电池安全保护、故障诊断、生命周期管理和防盗等。结合物联网技术与锂电池保护技术,设计出一款锂电池RFID保护板。保护板能够监测和记录锂电池工作时的电流、电压、温度等参数,为锂电池组提供实时的保护。另外,在需要的时候,保护板能够将测量数据通过ZigBee技术传输至监管平台,为锂电池的故障分析和维修提供决策依据,从而实现锂电池故障的在线测量和远程诊断。保护板通过记录锂电池的出厂、维修、用户ID等其他数据,同样也能够实现锂电池生命周期管理、安全防盗等管理功能。     

基于模糊控制的锂动力电池快速充电技术研究与实现

锂动力电池充电过程和具有多变量、离散性和非线性等特点,传统的PID控制很难从它的数学模型出发进行控制,而模糊控制技术可以不要数学模型进行控制。将模糊控制技术应用于充电控制,提出了模糊PID的充电模型,并与PID控制模型进行对比。仿真实验证明该充电模型具有充电时间短、鲁棒性强等特点,具有重要的实际意义和推广价值。     

MW级永磁同步电机无速度传感器矢量控制研究

针对电机实验站对永磁同步电机实验电源系统新的控制要求,研究了MW级永磁同步电机无速度传感器矢量控制方法,提出了一种基于模型参考自适应的速度辨识方法。该方法仅用q轴磁链误差信号构建自适应率辨识转速信息。首先通过Matlab仿真证明了该方法的有效性,然后进行了半直驱隐极式永磁同步风力发电机对拖实验,结果证明该方法能较好地辨识电机转速。     

超高面板堆石坝设计原则探讨

目前超高面板坝比较普遍地出现了面板沿垂直缝挤压破坏、顶部水平弯曲裂缝和水平拉伸裂缝等结构性破坏,为了防止这些事故的发生,探讨提出了超高面板坝设计原则。设计原则主要包括主堆石区压缩模量、主堆石区宽度、主次堆石压缩模量比、水库蓄水计划、设置堆石预沉降时间、分区堆石颗粒级配和面板设计等几个方面。     

MIMO-OFDM系统信道估计器的导频间隔优化设计

研究了导频子载波方式下,信道相干带宽、多普勒频移和信噪比对信道估计器的最优导频间隔设计的影响,以确定最优导频间隔。实验结果表明,要达到相同误码率性能,相干带宽越小的信道对应的信道估计器所需的最小导频间隔越小,而且在信道信噪比较低时,误码率不随导频间隔增加而线性增加,因此存在不为最小值的最优导频间隔。导频子载波方式适用于高速移动通信的最佳导频位置方式。     

Enhancing displacement of lead-zirconate-titanate (PZT) thin-film membrane microactuators via a dual electrode design

A common design of piezoelectric microactuators adopts a membrane structure that consists of a base silicon diaphragm, a layer of bottom electrode, a layer of piezoelectric thin film, and a layer of top electrode. In particular, the piezoelectric thin film is often made of lead-zirconate-titanate (PZT) for its high piezoelectric constants. When driven electrically, the PZT thin film extends or contracts flexing the membrane and generating an out-of-plane displacement. Many manufacturing defects, however, could significantly reduce the designed actuator displacement. Examples include residual stresses, warping, non-uniform etching of the silicon diaphragm, and misalignment between the top electrode and the silicon diaphragm. The purpose of this paper is to develop a dual top-electrode design to enhance the actuator displacement. In this design, the top electrode consists of two disconnected (thus independent) electrode areas, while a continuous bottom electrode serves as the ground. The two top electrodes are located in two regions with opposite curvature when the diaphragm deflects. When the two top electrodes are driven in an out-of-phase manner, the actuator displacement is enhanced. Finite element analyses and experimental measurements both confirm the feasibility of this design. When manufacturing defects are present, experimental results indicate that the actuator displacement can be optimized by adjusting the phase difference between the dual top electrodes.     

Achieving balance between proximity and diversity in multi-objective evolutionary algorithm

Currently, an alternative framework using the hypervolume indicator to guide the search for elite solutions of a multi-objective problem is studied in the evolutionary multi-objective optimization community very actively, comparing to the traditional Pareto dominance based approach. In this paper, we present a dynamic neighborhood multi-objective evolutionary algorithm based on hypervolume indicator (DNMOEA/HI), which benefits from both Pareto dominance and hypervolume indicator based frameworks. DNMOEA/HI is featured by the employment of hypervolume indicator as a truncation operator to prune the exceeded population, while a well-designed density estimator (i.e., tree neighborhood density) is combined with the Pareto strength value to perform fitness assignment. Moreover, a novel algorithm is proposed to directly evaluate the hypervolume contribution of a single individual. The performance of DNMOEA/HI is verified on a comprehensive benchmark suite, in comparison with six other multi-objective evolutionary algorithms. Experimental results demonstrate the efficiency of our proposed algorithm. Solutions obtained by DNMOEA/HI well approach the Pareto optimal front and are evenly distributed over the front, simultaneously.     

Illumination invariant extraction for face recognition using neighboring wavelet coefficients

The features of a face can change drastically as the illumination changes. In contrast to pose position and expression, illumination changes present a much greater challenge to face recognition. In this paper, we propose a novel wavelet based approach that considers the correlation of neighboring wavelet coefficients to extract an illumination invariant. This invariant represents the key facial structure needed for face recognition. Our method has better edge preserving ability in low frequency illumination fields and better useful information saving ability in high frequency fields using wavelet based NeighShrink denoise techniques. This method proposes different process approaches for training images and testing images since these images always have different illuminations. More importantly, by having different processes, a simple processing algorithm with low time complexity can be applied to the testing image. This leads to an easy application to real face recognition systems. Experimental results on Yale face database B and CMU PIE Face Database show that excellent recognition rates can be achieved by the proposed method.     

CrackTree: Automatic crack detection from pavement images

Pavement cracks are important information for evaluating the road condition and conducting the necessary road maintenance. In this paper, we develop CrackTree, a fully-automatic method to detect cracks from pavement images. In practice, crack detection is a very challenging problem because of (1) low contrast between cracks and the surrounding pavement, (2) intensity inhomogeneity along the cracks, and (3) possible shadows with similar intensity to the cracks. To address these problems, the proposed method consists of three steps. First, we develop a geodesic shadow-removal algorithm to remove the pavement shadows while preserving the cracks. Second, we build a crack probability map using tensor voting, which enhances the connection of the crack fragments with good proximity and curve continuity. Finally, we sample a set of crack seeds from the crack probability map, represent these seeds by a graph model, derive minimum spanning trees from this graph, and conduct recursive tree-edge pruning to identify desirable cracks. We evaluate the proposed method on a collection of 206 real pavement images and the experimental results show that the proposed method achieves a better performance than several existing methods.