Location and calculation-free node-scheduling schemes in large wireless sensor networks

In wireless sensor networks that consist of a large number of low-power, short-lived, unreliable sensors, one of the main design challenges is to obtain long system lifetime without sacrificing system original performance (sensing coverage and sensing reliability). To solve this problem, one of the potential approaches is to identify redundant nodes at the sensing interface and then assign them an off-duty operation mode that has lower energy consumption than the normal on-duty mode. In our previous work [J. Wireless Commun. Mobile Comput. 3 (2003) 271; Processing of ACM Wireless Sensor Network and Application Workshop 2002, September 2002], we proposed a node-scheduling scheme, which can provide a 100% sensing coverage preservation capability. This, however, requires each node to be aware of its own and its neighbors’ location information. Also, in that scheme, each node has to do accurate geometrical calculation to determine whether to take an off-duty status. In this paper, we propose and study several alternative node-scheduling schemes, which cannot completely preserve the original system coverage, but are nonetheless light-weighted and flexible compared with the previous one. Our simulation results compare these schemes with the previous one and demonstrate their effectiveness.     

基于C8051F040的CAN总线通讯系统设计

作为一种很有前途的现场总线,CAN总线通信相对于一般的串行通信总线,他的实时性、可靠性和灵活性的特点更加突出。介绍了C8051F040内部集成的CAN控制器的使用方法,给出了以此芯片为核心的CAN总线智能节点通讯接口的硬件和软件设计,实际的运行验证了设计的正确性。其设计通用性好、可靠性高、性价比高,具有广泛的应用前景。     

Feedback predictive control for constrained fuzzy systems with Markovian jumps

In this paper, a feedback model predictive control method is presented to tackle control problems with constrained multivariables for uncertain discrete‐time nonlinear Markovian jump systems. An uncertain Markovian jump fuzzy system (MJFS) is obtained by employing the Takagi‐Sugeno (T‐S) fuzzy model to represent a discrete‐time nonlinear system with norm bounded uncertainties and Markovain jump parameters. To achieve more generality, the transition probabilities of the Markov chain are assumed to be partly unknown and partly accessible. The predictive formulation adopts an on‐line optimization paradigm that utilizes the closed‐loop state feedback controller and is solved using the standard semi‐definite programming (SDP). To reduce the on‐line computational burden, a mode independent control move is calculated at every sampling time based on a stochastic fuzzy Lyapunov function (FLF) and a parallel distributed compensation (PDC) scheme. The robust mean square stability, performance minimization and constraint satisfaction properties are guaranteed under the control move for all admissible uncertainties. A numerical example is given to show the efficiency of the developed approach. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

无位置信息的无线传感器网络漏洞发现算法*

无线传感器网络广泛应用的前提是能够检监测目标区域特定事件的发生。而节点能量受限、难补给的特点导致监测性能难以保证,造成监测盲区出现。针对无线传感器网络节点的地理位置信息未知的情况进行了研究,提出覆盖漏洞发现算法CHDA (Coverage Holes Discovery Algorithm),在以节点为极点建立的极坐标中计算出相邻节点的相对位置信息和该节点被其邻居节点覆盖的边缘弧的信息,并且根据节点的单纯覆盖弧序列的定义计算出节点被其邻居节点覆盖的单纯覆盖弧序列,从而得到网络中的覆盖漏洞。覆盖漏洞的发现克服的地理位置信息未知的限制,为漏洞的修补提供了必要的前提条件进而保证传感器网络的覆盖率。     

随机非线性和部分转移概率未知时马尔科夫系统的H1控制

研究上述系统时: 1) 利用了非线性的概率分布信息; 2) 利用了转移概率中已知部分和未知部分的关系. 利用李雅普诺夫泛函方法和线性矩阵不等式方法, 本文得到了使得系统随机稳定的充分条件并得到了相应的反馈控制增益. 文中最后给出的例子表明了所建立模型和分析方法的有效性.     

基于UKF 和神经网络的一类非线性系统状态估计

在模型未知的情况下,估计过程的重要变量尤为重要.鉴于此,采用不敏卡尔曼滤波(UKF)与神经网络相结合的方法,解决一类未知模型非线性系统的状态估计问题.采用动态神经网络对非线性系统进行建模,利用UKF对状态和权值进行同时更新,从而达到神经网络逼近真实模型,估计值跟随真实值的目的.通过两个仿真实例表明了所提出的方法具有良好的估计效果,并且状态在输出中的比重越大,其估计精度越高.     

基于移动锚节点与多级通信的三维传感器网络节点自定位算法研究

在基于移动锚节点的三维传感器网络节点自定位算法SNLSFA(Sensor Node Localization Scheme based on Flying Anchors)的基础上,提出了一种新的基于移动锚节点与多级通信的三维传感器网络节点自定位算法SNLSFAMC(Sensor Node Localization Scheme based on Flying Anchors and Multi-level Communication)。首先由移动锚节点提供3个或4个辅助定位点,再由辅助定位点得到两条非平行线段,然后过线段中点分别做垂直于线段的平面,经两平面相交后得到一条经过待定位节点的直线,最后利用辅助定位点与待定位节点之间的距离作为通信半径即可得到待定位节点的位置。仿真结果表明,与SNLSFA相比,在相同锚节点数量下,SNLSFAMC提高了定位精度,且在相同定位精度下,SNLSFAMC降低了对锚节点数量的需求,提高了算法的响应时间。     

Sliding mode control for Markov jump linear uncertain systems with partly unknown transition rates

In this paper, based on sliding mode control approach, the robust stabilisation problem for a class of continuous-time Markovian jump linear uncertain systems with partly unknown transition rates is investigated. The transition rate matrix under consideration covers completely known, boundary known and completely unknown elements. By making use of linear matrix inequalities technique, sufficient conditions are presented to derive the linear switching surface and guarantee the stochastic stability of sliding mode dynamics. Then a sliding mode control law is designed to drive the state trajectory of the closed-loop system to the specified linear switching surface in finite time in spite of the existing uncertainties and unknown transition rates. Finally, an example is given to verify the validity of the theoretical results.     

Feedback nonlinear discrete-time systems

In this paper, we design an adaptive iterative learning control method for a class of high-order nonlinear output feedback discrete-time systems with random initial conditions and iteration-varying desired trajectories. An n-step ahead predictor approach is employed to estimate future outputs. The discrete Nussbaum gain method is incorporated into the control design to deal with unknown control directions. The proposed control algorithm ensures that the tracking error converges to zero asymptotically along the iterative learning axis except for the beginning outputs affected by random initial conditions. A numerical simulation is carried out to demonstrate the efficacy of the presented control laws.