基于行车风压的谐振腔式压电俘能器研究

针对汽车行驶过程中车载传感器需要持续稳定的供能需求,设计一种以谐振腔结构为主体,扰流圆柱与亥姆霍兹谐振腔为辅助的压电俘能器。为研究俘能器的发电性能,设计其最佳结构,建立流固电耦合仿真模型。仿真时,根据实际风压分布,将风压载荷分区加载到压电发电模块。仿真分析结果表明,基板与压电陶瓷的厚度比对压电悬臂梁的输出电压和固有频率有影响,最佳厚度比为1.25;基板与主腔体间间隙、扰流圆柱直径、亥姆霍兹谐振腔皆存在最佳尺寸参数使压电俘能器发电性能达到最佳;负载电阻在400～600 kΩ内时,可获得最佳的输出功率;风速为15 m/s时,最大输出功率为37.3 mW。     

非接触PVDF压电悬臂梁风能收集系统特性研究

设计了一款由聚偏氟乙烯高聚物(PVDF)三角形压电梁、永磁体和环形基座所构成的非接触式压电风能收集系统,并研究了该系统的风能收集特性。首先分析了系统中单个拥磁梁振动时所受的激励,得到其波形为周期性三角波;其次,通过有限元仿真分析可知,当激励频率和拥磁梁的一阶固有频率相同时,其输出功率最大,并由实验得知,单梁工作系统适用于风速小于3.2m/s的环境,测得工作中的风速为2.8m/s时,激励频率近似拥磁梁的固有频率(为17.4Hz),系统获得的最大输出功率为12.4μW。最后,利用夹持长度可调的多个拥磁梁所连接成的阵列,实现了拓宽系统风速范围的目的,并以夹持长度不同的4个梁实验为例,验证了系统在风能收集时风速的工作范围为2.80～5.25m/s,对应的平均功率为13.6μW。     

分集MIMO 雷达布站研究

分集多输入多输出(MIMO)雷达利用目标的空间散射多样性克服由于目标雷达散射截面积闪烁引起的漏警,为隐身目标有效探测提供解决办法。分集MIMO 雷达布站方式对性能影响很大,文中以相控阵体制雷达为研究对象,研究其组成分集MIMO 雷达时的规模、站间距、单站威力差异约束和布站方式等,并通过蒙特卡罗仿真给出了布站建议。     

基于GaN HEMT的混合EF类功率放大器设计

基于GaN HEMT 提出了一种3.5GHz 频率的高效率混合EF 类功率放大器。采用输出端并联谐振电感来补偿晶体管的输出电容,提高了混合EF类功放的工作频段,使其能在3.5GHz 频率下达到高效率。使用简单的微带传输线构成谐波控制和匹配网络,满足混合EF类功放基波、奇次谐波和偶次谐波的阻抗特征,同时完成与50Ω负载阻抗的匹配。测试结果显示,在3.3 ～ 3.8GHz 频段内,在1dB压缩点处输出功率达到40dBm,漏极效率75%～79%,增益11.5～12.3dB。     

基于计算机视觉的原木高出材率的研究

计算机能模拟人眼视觉环境来甄别、测量视野中的事物,随着精度的不断提高,计算机视觉能代替人眼实现简单、重复的人工操作。计算机视觉结合原木剖料可以提高原木的出材率,减少木材损耗。凭借计算机的高效性、准确性,计算机视觉能实现原木的最大利用,最大程度减少生成方木浪费的原料, 提高原木出材率。将所提算法应用于自动化带锯原木切割系统的基本流程是通过图像预处理来消除图像噪声,通过颜色分割去除背景,通过边缘检测给出感兴趣区域的轮廓,应用形态学运算连接和填补被误处理的轮廓边缘,计算最大面积的拟合椭圆。实验结果表明,该算法可以满足实际生产的需要,实验精度达到95%以上。     

Adaptive Iterative Learning Boundary Control of a Flexible Manipulator with Guaranteed Transient Performance

This paper investigates the iterative learning control (ILC) problem of a flexible manipulator in the presence of external disturbances and output constraints. The dynamic behavior of the flexible manipulator is represented by partial differential equations (PDEs). We propose an ILC law to track the desired trajectory and suppress the vibration of the elastic deflection. The control scheme is based on a prescribed performance bound (PPB) which characterizes the maximum restrictions and convergence rate of the tracking error and deflection error. It is shown that the errors satisfy the prescribed performance bond all the time at any iterations. The established theoretical results are illustrated using numerical simulations for control performance verification.     

Exponential Synchronization of Memristive Chaotic Recurrent Neural Networks Via Alternate Output Feedback Control

This paper considers the global exponential synchronization problem of two memristive chaotic recurrent neural networks with time‐varying delays using periodically alternate output feedback control. First, the periodically alternate output feedback control rule is designed for the global exponential synchronization of two memristive chaotic recurrent neural networks. Then, according to the Lyapunov stability theory, we construct an appropriate Lyapunov‐Krasovskii functional to derive several new sufficient conditions guaranteeing exponential synchronization of two memristive chaotic recurrent neural networks under periodically alternate output feedback control. Compared with existing results on synchronization conditions on the basis of linear matrix inequalities of memristive chaotic recurrent neural networks, the derived results complement, extend earlier related results, and are also easy to validate in this paper. An illustrative example is provided to illustrate the effectiveness of the synchronization criteria.     

Output‐feedback model predictive control for stochastic systems with multiplicative and additive uncertainty

This paper studies the output‐feedback model predictive control (MPC) design problem for linear systems with multiplicative and additive random uncertainty. We first present an off‐line optimization algorithm to optimize feedback gains of the observer and the dual‐mode control policy. After that, by defining a cuboid tube whose center and boundary are both time‐varying variables, we develop a set sequence with increased freedom to contain stochastic system trajectories. A quadratic performance function with analytic upper and lower bounds is minimized such that it decreases exponentially to a finite range under the expectation. The resulting MPC algorithms are proved to guarantee practically stochastic input‐to‐state stability. A numerical example of the wind turbine model illustrates the properties of the MPC algorithms.     

Cooperative robust output regulation problem for discrete‐time linear time‐delay multi‐agent systems

In this paper, we study the cooperative robust output regulation problem for discrete‐time linear multi‐agent systems with both communication and input delays by a distributed internal model approach. We first introduce the distributed internal model for discrete‐time multi‐agent systems with both communication and input delays. Then, we define the so‐called auxiliary system and auxiliary augmented system. Finally, we solve our problem by showing, under some standard assumptions, that if a distributed state feedback control or a distributed output feedback control solves the robust output regulation problem of the auxiliary system, then the same control law solves the cooperative robust output regulation problem of the original multi‐agent systems.     

Output consensus for heterogeneous multiagent systems with Markovian switching network topologies

This paper investigates the output consensus problem of heterogeneous continuous‐time multiagent systems under randomly switching communication topologies. The switching mechanism is governed by a time‐homogeneous Markov process, whose states correspond to all possible communication topologies among agents. A novel dynamic consensus controller is proposed. The controller gains are designed based on the information of the expectation graph and the solutions to regulator equations. Furthermore, a necessary and sufficient condition is presented for output consensus of the controlled multiagent system in mean square sense. Finally, a simulation example is provided to corroborate the effectiveness of the proposed controller.