分布式传感网络的自适应一致性算法

为了提高分布式传感网络的估计精度;提出了一种新的自适应一致性算法。该算法在每次迭代时只需部分节点工作;即进行目标状态的监测。通过节点之间二进制信息的交换来调整每次迭代时的一致性权值;使得每次迭代时工作节点所占的权值更大;进而将该一致性算法与卡尔曼滤波相结合对目标状态进行估计。对该算法进行数值仿真;并与其他一致性加权算法进行比较;验证了该算法的有效性。     

线性时不变系统的分布式固定时间观测器

针对线性时不变系统的分布式状态估计问题,基于双极限加权齐次估计理论和可观测性分解方法提出一类分布式固定时间收敛观测器.首先,针对单输入单输出积分链式系统,使用双极限加权齐次性方法设计集中式固定时间观测器.然后,基于可观测性分解将线性时不变系统分为可观测和不可观测子系统,传感器网络中每个智能体以集中式观测器为基础,在固定时间内仅用系统输出测量值重构局部可观子状态,利用智能体间状态信息构造一致性算法在固定时间内估计出局部不可观测子状态,从而在固定时间内实现状态全知.不同于已有工作,所提出观测器不需要构造具体的李雅普诺夫函数即可给出收敛时间的显示表达式.最后,通过仿真实验验证所设计观测器的有效性.     

传感器网络中的分布式滚动时域状态估计

利用分布式滚动时域方法对无线传感器网络的状态估计问题进行研究,给出了基于量化测量值的滚动时域估计算法。在无线传感器网络的环境下处理分布式状态估计问题时,减少通信的成本是非常重要的一个环节,需要将观测值量化后再传送。以往的滚动时域估计方法无法处理量化观测值的状态估计问题,而本文的方法考虑了最严格的观测值量化情况即传感器只发送一个比特至融合中心的状态估计问题。与其它传感器网络中的状态估计方法相比,该方法减少了每一步的计算量。仿真结果验证了该算法的有效性。     

Design of consensus and adaptive consensus filters for distributed parameter systems

This work establishes an abstract framework that considers the distributed filtering of spatially varying processes using a sensor network. It is assumed that the sensor network consists of groups of sensors, each of which provides a number of state measurements from sensing devices that are not necessarily identical and which only transmit their information to their own sensor group. A modification to the local spatially distributed filters provides the non-adaptive case of spatially distributed consensus filters which penalize the disagreement amongst themselves in a dynamic manner. A subsequent modification to this scheme incorporates the adaptation of the consensus gains in the disagreement terms of all local filters. Both the well-posedness of these two consensus spatially distributed filters and the convergence of the associated observation errors to zero in appropriate norms are presented. Their performance is demonstrated on three different examples of a diffusion partial differential equation with point measurements.     

Kullback–Leibler average,consensus on probability densities,and distributed state estimation with guaranteed stability

This paper addresses distributed state estimation over a sensor network wherein each node–equipped with processing, communication and sensing capabilities–repeatedly fuses local information with information from the neighbors. Estimation is cast in a Bayesian framework and an information-theoretic approach to data fusion is adopted by formulating a consensus problem on the Kullback–Leibler average of the local probability density functions (PDFs) to be fused. Exploiting such a consensus on local posterior PDFs, a novel distributed state estimator is derived. It is shown that, for a linear system, the proposed estimator guarantees stability, i.e. mean-square boundedness of the state estimation error in all network nodes, under the minimal requirements of network connectivity and system observability, and for any number of consensus steps. Finally, simulation experiments demonstrate the validity of the proposed approach.     

整车主动悬架系统分布式滚动时域一致性估计

考虑整车主动悬架系统的约束状态估计问题,本文提出基于一致性原理的分布式滚动时域估计(DMHE)算法.首先,为了降低状态估计过程中的计算量,将整车主动悬架系统分解为若干降阶子系统.其次,为提高分布式状态估计效果,采用滚动时域估计(MHE)方法处理主动悬架系统的状态和噪声约束.考虑子系统与邻居估计状态的相关性,在采样间隔中执行多次一致性原理实现主动悬架系统状态的信息融合,进一步建立了算法的稳定性充分条件.最后,通过对比仿真实验验证算法的有效性和优越性.     

传感器网络一致性分布式滤波算法

为了改善分布式传感器网络的估计性能,提出了一种基于状态预测一致的滤波算法.在对局部估计值进行一致化处理的基础上,重点研究了利用邻居节点前一时刻的估计值对当前局部状态预测值进行修正来提高估计精度.给出了一种一致性增益的选择方法,利用李雅普诺夫方法得到了算法收敛的充分条件,并讨论了影响算法收敛速度的因素.仿真结果表明了算法的有效性,并发现节点度较大的传感器在网络估计中发挥着重要作用,可通过调整这类节点的一致性系数来改善算法性能.     

不确定通信拓扑下多智能体系统鲁棒一致性

本文研究一类具有通信不确定的多智能体系统鲁棒一致性问题.本文提出基于标称通信拓扑有向生成树的线性变换方法,将线性多智能体系统的状态一致性问题转化为相应线性系统的鲁棒部分变元渐近稳定性问题.首先采用基于有向生成树关联矩阵的线性变换,将多智能体系统网络的全局状态方程转化为一个降阶子系统;其次,将拉普拉斯矩阵的摄动部分进行分解,利用降阶系统设计鲁棒二次镇定控制器,推导出所有智能体状态达到渐近一致的充分条件.在此基础上将控制协议的参数设计转化为求解线性矩阵不等式的可行解.最后,通过数值仿真验证了所提出的一致性协议分析与设计方法的可行性和有效性.     

用神经网络估计模型误差的预测滤波算法

针对时不变非线性系统,提出一种用神经网络进行模型误差估计的预测滤波算法．该算法用寻优的方法离线获得与当前状态和下一步输出测量相对应的模型误差估值,并作为样本训练神经网络;实际滤波中,用训练好的神经网络进行模型误差估计．该方法与原预测滤波算法相比没有动态过程,不会因为滤波器初始误差太大而振荡或发散,且稳态精度与计算步长无关．通过对一个二阶非线性系统的仿真验证了神经一预测滤波器的优越性。     

An event-triggered approach to state estimation with multiple point- and set-valued measurements

In this work, we consider state estimation based on the information from multiple sensors that provide their measurement updates according to separate event-triggering conditions. An optimal sensor fusion problem based on the hybrid measurement information (namely, point- and set-valued measurements) is formulated and explored. We show that under a commonly-accepted Gaussian assumption, the optimal estimator depends on the conditional mean and covariance of the measurement innovations, which applies to general event-triggering schemes. For the case that each channel of the sensors has its own event-triggering condition, closed-form representations are derived for the optimal estimate and the corresponding error covariance matrix, and it is proved that the exploration of the set-valued information provided by the event-triggering sets guarantees the improvement of estimation performance. The effectiveness of the proposed event-based estimator is demonstrated by extensive Monte Carlo simulation experiments for different categories of systems and comparative simulation with the classical Kalman filter.