丢包和量化约束下的不确定系统分布式滚动时域估计

研究了数据丢包和量化约束下的随机不确定系统分布式状态估计问题.将丢包现象描述为随机Bernoulli序列,采用预测补偿机制对数据丢包进行补偿,将量化引入的误差转化为观测方程中的不确定参数,将系统的模型不确定性描述为系数矩阵受到随机扰动;利用固定时域内的所有观测值构造代价函数,将状态估计问题建模为带不确定参数的鲁棒最小二...     

不确定系统的滚动时域H∞控制设计

针对存在状态矩阵、输入矩阵结构化摄动和外成动输入等不确定性的连续线性时变系统,给出滚动时域Ｈ＾∞控制律及其存在性的充分条件。该控制律使系统闭环稳定,且系统对扰动输入的增益不超过某一人为设定的上界。进一步考虑执行机构非线性特性,推导出上述系统当执行机构存在扇区非线性摄动时的稳定滚动时域Ｈ＾∞控制律及其存在性的充分条件。     

噪声方差不确定约束系统的滚动时域估计

针对噪声方差不确定的约束系统,讨论了一种鲁棒滚动时域估计(MHE)方法.首先,根据噪声方差不确定模型,找到满足所有不确定性的最小方差上界,在线性矩阵不等式(LMI)框架下求解优化问题,得到近似到达代价的表达形式;然后再融合预测控制的滚动优化原理,把系统的硬约束直接表述在优化问题中,在线优化性能指标,估计出当前时刻系统的状态.仿真时与鲁棒卡尔曼滤波方法进行比较,结果表明了该方法的有效性.     

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.     

不确定系统的滚动时域H ∞控制设计

针对存在状态矩阵、输入矩阵结构化摄动和外界扰动输入等不确定性的连续线性时变系统,给出滚动时域H     

基于滚动时域估计的非线性目标跟踪*

实际的雷达跟踪问题大多属于非线性问题,存在着各种物理约束,采用基于在线滚动优化原理的滚动时域估计方法可以有效地处理带约束非线性目标跟踪问题。滚动时域估计通过引入到达代价函数,将非线性跟踪滤波问题转换为带约束的有限时域优化问题,可以有效减少优化问题求解的计算量,能够显著提高状态估计的准确度。针对实际的雷达跟踪问题,仿真结果表明,滚动时域估计能有效地提高非线性目标跟踪的精度。     

网联车辆协同巡航系统快速滚动时域状态估计

考虑云平台监控下的网联车辆协同自动巡航控制(CACC)系统,提出一种快速滚动时域估计方法.采用网联车队纵向动力学模型描述网联车辆CACC系统,降低网联车辆CACC系统的状态能观性要求.再应用块概念设计滚动时域估计算法的噪声块结构,压缩滚动时域估计问题的优化变量个数,从而减少其在线计算量.进一步,应用李雅普诺夫稳定性定理...     

具有数据包丢失的离散随机不确定系统的线性最优满阶估值器

研究了具有数据包丢失和随机不确定性离散随机线性系统的状态估计问题.其中数据包丢失是随机的,且满足Bernoulli分布,系统矩阵中的随机不确定性由一个白色乘性噪声来描述.首先,通过配方方法,提出了最小均方意义下的无偏最优线性递推满阶滤波器.所提出的滤波器用到了当前时刻和最近时刻接收到的观测来保证线性最优性.与多项式滤波和增广滤波器相比,本文的滤波器具有较小的计算负担.然后,基于所获得的线性滤波器推导了线性最优预报器和平滑器.进一步研究了线性最优估值器的渐近稳定性,给出了稳态特性存在的一个充分条件.最后,通过两个仿真例子验证了所提估计算法的优越性.     

滚动时域估计在连续搅拌釜式反应器中的应用研究

针对连续搅拌釜式反应器的多变量、非线性、带约束等特点,设计一种基于滚动优化原理的滚动时域估计方法.对比扩展卡尔曼滤波和滚动时域估计两种方法,在滚动时域估计中采用扩展卡尔曼滤波近似代替到达代价函数,并通过改变滚动时域窗口的大小有效地减小估计过程中的误差.仿真结果表明:滚动时域估计优于扩展卡尔曼滤波,能够有效地处理带约束化...     

基于UKF的滚动时域估计方法研究

在实际工业过程中,模型参数往往具有一定的时变性和非线性。为了能够有效地实施过程操作优化,常常要对过程模型参数进行在线估计。滚动时域估计方法是解决非线性系统模型参数在线估计的1种实用方法。滚动时域估计方法的关键问题之一是抵达成本(Arrival Cost)的计算,针对简化计算抵达成本带来的精度问题,提出采用无迹卡尔曼滤波(UKF)算法来近似估算目标函数中的抵达成本。最后,将基于UKF的滚动时域估计方法应用于2个例子中。结果表明,基于UKF的滚动时域估计方法具有较好的估计效果。     

Constrained state estimation for stochastic jump systems: moving horizon approach

We discuss the state estimation advantages for a class of linear discrete-time stochastic jump systems, in which a Markov process governs the operation mode, and the state variables and disturbances are subject to inequality constraints. The horizon estimation approach addressed the constrained state estimation problem, and the Bayesian network technique solved the stochastic jump problem. The moving horizon state estimator designed in this paper can produce the constrained state estimates with a lower error covariance than under the unconstrained counterpart. This new estimation method is used in the design of the restricted state estimator for two practical applications.     

Robust state estimation for uncertain discrete-time stochastic systems with missing measurements

In this paper, results of robust estimation of Zhou (2010a) are extended to state estimation with missing measurements. A new procedure is derived which inherits the main properties of that of Zhou (2010a). In this extension, a covariance matrix used in the recursions is replaced by its estimate which makes its asymptotic property investigation mathematically difficult. Though introducing a monotonic function and using the so-called squeeze rule, this new robust estimator is proved to converge to a stable system. Numerical simulation results indicate that the proposed estimator may have an estimation accuracy better than the estimator of Wang, Yang, Daniel, and Liu (2005).     

Moving horizon estimation for switching nonlinear systems

This paper is concerned with moving horizon estimation for a class of constrained switching nonlinear systems, where the system mode is regarded as an unknown discrete state to be estimated together with the continuous state. In this work, we establish the observability framework of switching nonlinear systems by proposing a series of concepts about observability and analyzing the properties of such concepts. By fully applying the observability properties, we prove the stability of the proposed moving horizon estimators. Simulation results are reported to verify the derived results.     

测量数据丢失的随机不确定离散系统的鲁棒H2状态估计

由于频宽有限,或者传感器临时损坏,测量数据在网络中传输时可能会丢失.本文对一类测量数据丢失的不确定离散系统,研究了鲁棒H2状态估计问题.所有的系统矩阵的参数都属丁二给定的凸多面体区域.测量数据的丢失是随机发生的,认为它是已知概率的Bernoulli随机序列.对于所有容许的不确定和可能的数据丢失,采用线性矩阵不等式方法,给出了全阶和降阶的H2滤波器存在的充分条件.数值仿真表明本文所提方法的有效性.     

Distributed moving horizon estimation for nonlinear constrained systems

In this paper we consider a nonlinear constrained system observed by a sensor network and propose a distributed state estimation scheme based on moving horizon estimation (MHE). In order to embrace the case where the whole system state cannot be reconstructed from data available to individual sensors, we resort to the notion of MHE detectability for nonlinear systems, and add to the MHE problems solved by each sensor a consensus term for propagating information about estimates through the network. We characterize the error dynamics and provide conditions on the local exchanges of information in order to guarantee convergence to zero and stability of the state estimation error provided by any sensor. Copyright © 2010 John Wiley & Sons, Ltd.     

随机时滞网络控制系统的后退时域估计

本文考虑具有随机观测时滞系统的后退时域估计问题.首先,针对随机时滞网络控制系统,运用观测重组技术,将带有时滞的观测方程转化为无时滞观测方程,得到一组新的无时滞观测序列.在此基础上,运用线性最小方差无偏估计理论,推导出后退时域估计器的批形式公式和迭代形式公式,并给出稳定性分析.通过具体的仿真实例,对比现有卡尔曼滤波器,验证了所提出的后退时域估计器具有更好的跟踪能力.     

A new approach to constrained state estimation for discrete‐time linear systems with unknown inputs

This paper addresses the problem of estimating the state for a class of uncertain discrete‐time linear systems with constraints by using an optimization‐based approach. The proposed scheme uses the moving horizon estimation philosophy together with the game theoretical approach to the filtering to obtain a robust filter with constraint handling. The used approach is constructive since the proposed moving horizon estimator (MHE) results from an approximation of a type of full information estimator for uncertain discrete‐time linear systems, named in short ‐MHE and –full information estimator, respectively. Sufficient conditions for the stability of the ‐MHE are discussed for a class of uncertain discrete‐time linear systems with constraints. Finally, since the ‐MHE needs the solution of a complex minimax optimization problem at each sampling time, we propose an approximation to relax the optimization problem and hence to obtain a feasible numerical solution of the proposed filter. Simulation results show the effectiveness of the robust filter proposed.     

Receding horizon output feedback control for constrained uncertain systems using periodic invariance

In this article, we consider a receding horizon output feedback control (RHOC) method for linear discrete-time systems with polytopic model uncertainties and input constraints. First, we derive a set of estimator gains and then we obtain, on the basis of the periodic invariance, a series of state feedback gains stabilising the augmented output feedback system with these estimator gains. These procedures are formulated as linear matrix inequalities. An RHOC strategy is proposed based on these state feedback and state estimator gains in conjunction with their corresponding periodically invariant sets. The proposed RHOC strategy enhances the performance in comparison with the case in which static periodic gains are used, and increases the size of the stabilisable region by introducing a degree of freedom to steer the augmented state into periodically invariant sets.