Simultaneous state and input estimation of hybrid systems with unknown inputs

This paper addresses the problem of the simultaneous state and input estimation for hybrid systems when subject to input disturbances. The proposed algorithm is based on the moving horizon estimation (MHE) method and uses mixed logical dynamical (MLD) systems as equivalent representations of piecewise affine (PWA) systems. So far the MHE method has been successfully applied for the state estimation of linear, hybrid, and nonlinear systems. The proposed extension of the MHE algorithm enables the estimation of unknown inputs, or disturbances, acting on the hybrid system. The new algorithm is shown to improve the convergence characteristics of the MHE method by reducing the delay of convergent estimates, while assuring convergence for every possible sequence of input disturbances. To ensure convergence the system is required to be incrementally input observable, which is an extension to the classical incremental observability property.     

基于条件高斯分布的未知输入与状态估计算法

针对未知输入同时存在于系统方程和测量方程的直接馈通线性随机系统, 提出了一种同时估计未知输入 和状态的算法. 首先, 通过将未知输入模型描述为有限方差的高斯分布, 利用条件高斯分布的性质, 推导出新的滤波 算法, 以同时得到未知输入估计和状态估计. 其次, 证明了当未知输入的方差趋于无穷大时, 本文提出的算法等价于 已有的递归三步滤波算法. 最后, 分析了本文算法的渐进稳定性条件, 结果表明, 与已有算法相比, 本文的算法适用 范围更广.     

Event-based input and state estimation for linear discrete time-varying systems

In this paper, the joint input and state estimation problem is considered for linear discrete-time stochastic systems. An event-based transmission scheme is proposed with which the current measurement is released to the estimator only when the difference from the previously transmitted one is greater than a prescribed threshold. The purpose of this paper is to design an event-based recursive input and state estimator such that the estimation error covariances have guaranteed upper bounds at all times. The estimator gains are calculated by solving two constrained optimisation problems and the upper bounds of the estimation error covariances are obtained in form of the solution to Riccati-like difference equations. Special efforts are made on the choices of appropriate scalar parameter sequences in order to reduce the upper bounds. In the special case of linear time-invariant system, sufficient conditions are acquired under which the upper bound of the error covariance of the state estimation is asymptomatically bounded. Numerical simulations are conducted to illustrate the effectiveness of the proposed estimation algorithm.     

Adaptive model predictive control for a class of constrained linear systems based on the comparison model

This paper proposes an adaptive model predictive control (MPC) algorithm for a class of constrained linear systems, which estimates system parameters on-line and produces the control input satisfying input/state constraints for possible parameter estimation errors. The key idea is to combine the robust MPC method based on the comparison model with an adaptive parameter estimation method suitable for MPC. To this end, first, a new parameter update method based on the moving horizon estimation is proposed, which allows to predict an estimation error bound over the prediction horizon. Second, an adaptive MPC algorithm is developed by combining the on-line parameter estimation with an MPC method based on the comparison model, suitably modified to cope with the time-varying case. This method guarantees feasibility and stability of the closed-loop system in the presence of state/input constraints. A numerical example is given to demonstrate its effectiveness.     

Maximum likelihood parameter estimation algorithm for controlled autoregressive autoregressive models

A maximum likelihood parameter estimation algorithm is derived for controlled autoregressive autoregressive (CARAR) models based on the maximum likelihood principle. In this derivation, we use an estimated noise transfer function to filter the input–output data. The simulation results show that the proposed estimation algorithm can effectively estimate the parameters of such class of CARAR systems and give more accurate parameter estimates than the recursive generalized least-squares algorithm.     

Input estimation with multiple model for maneuvering target tracking

To increase the performance of maneuvering target tracking, an algorithm utilizing input estimation with multiple model based on two independent mode sets is suggested in this paper. The proposed algorithm consists of hypothesized multiple filters to estimate the unknown target acceleration and a test statistic developed from a modified version of the generalized likelihood ratio test to detect the maneuver onset time. An efficient algorithm for the target acceleration estimation is derived to reduce the computational burden of multiple model estimation. A numerical analysis is carried out to obtain the proper window length and the average delay of the algorithm. Performance of the proposed algorithm is evaluated by a series of simulation runs.     

基于遗传算法的非线性系统时变时滞的在线估计方法

针对系统输入带有纯时滞的一类非线性系统,选择有限点的的输出误差的平方和构成适应度函数,采用十进制编码技术,提出一种基于遗传算法的非线性系统时变时滞的在线估计方法,该方法具有一定的抗噪声能力。仿真实验结果验证了所提出方法的有效性。     

SIMO信道下MPSK信号的载波相位盲估计

提出了一种SIMO信道下多个MPSK信号的载波相位联合盲估计算法,以提高低信噪比条件下载波相位的估计性能.该算法综合利用多路接收信号以去除调制信息对相位估计的影响,构建与各信号相位相关的多个方程,并最终通过加权最小二乘解算方法完成对信号相位的联合估计.仿真结果表明,与传统的V&V算法相比,本算法在低信噪比条件下具有更加...     

A cumulant based algorithm for the identification of input-output quadratic systems

The identification of a special class of polynomial models is pursued in this paper. In particular a parameter estimation algorithm is developed for the identification of an input-output quadratic model excited by a zero mean white Gaussian input and with the output corrupted by additive measurement noise. Input-output crosscumulants up to the fifth order are employed and the identification problem of the unknown model parameters is reduced to the solution of successive triangular linear systems of equations that are solved at each step of the algorithm. Simulation studies are carried out and the proposed methodology is compared with two least squares type identification algorithms, the output error method and a combination of the instrumental variables and the output error approach. The proposed cumulant based algorithm and the output error method are tested with real data produced by a robotic manipulator.     

有色噪声干扰输出误差系统的偏差补偿递推最小二乘辨识方法

借助于偏差补偿原理和预滤波思想, 推导了有色噪声干扰输出误差系统参数估计的偏差补偿递推最小二乘 (Bias compensation recursive least squares, BCRLS) 辨识方法. 该方法降低了辨识对输入信号平稳性的要求, 实现了偏差补偿方法参数估计的递推计算, 可以用于在线辨识. 提出的递推 BCRLS 辨识方法优于非递推偏差补偿最小二乘算法, 提高了参数估计精度. 仿真试验证实了算法的有效性.     

Blind Joint Maximum Likelihood Channel Estimation and Data Detection for SIMO Systems

A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single- input multiple-output (SIMO) systems.The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop.An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model,and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence.A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems.     

Adaptive feedback linearizing control of linear induction motor considering the end-effects

This paper proposes an input–output feedback linearization techniques for linear induction motors, taking into consideration the dynamic end-effects. As a main original content, this work proposes a new control law based on the on-line estimation of the induced-part time constant. The estimation law is obtained thanks to a Lyapunov based analysis and thus the stability of the entire control system, including the estimation algorithm, is intrinsically guaranteed. Moreover, with such an approach even the on-line variation of the induced-part time constant with the speed is retrieved, thus improving the behavior of previously developed approaches where such a variation vs. speed is considered a priori known. The proposed control technique, integrating the on-line induced-part time constant estimation, is tested by means of simulations and experiments carried out on a suitably developed test set-up.     

Decomposition‐based multiinnovation gradient identification algorithms for a special bilinear system based on its input‐output representation

This article considers the parameter estimation for a special bilinear system with colored noise. Its input‐output representation is derived by eliminating the state variables in the bilinear system. Based on the input‐output representation of the bilinear system, a multiinnovation generalized extended stochastic gradient (MI‐GESG) algorithm is proposed by using the multiinnovation identification theory. Furthermore, a decomposition‐based multiinnovation (ie, hierarchical multiinnovation) generalized extended stochastic gradient identification (H‐MI‐GESG) algorithm is derived to enhance the parameter estimation accuracy by using the hierarchical identification principle, and a GESG algorithm is presented for comparison. Compared with the existing identification algorithms for the bilinear system, the proposed MI‐GESG and H‐MI‐GESG algorithms can generate more accurate parameter estimation. Finally, a simulation example is provided to verify the effectiveness of the proposed algorithms.     

Identification of linear systems with hard input nonlinearities of known structure

This paper studies identification of systems with input nonlinearities of known structure. For input nonlinearities parameterized by one parameter, a deterministic approach is proposed based on the idea of separable least squares. The identification problem is shown to be equivalent to an one-dimensional minimization problem. The method is very effective for several common static and nonstatic input nonlinearities. For a general input nonlinearity, a correlation analysis based identification algorithm is presented which is shown to be convergent.     

基于无偏线性最优估计的PET图像重建

正电子发射断层成像(Positron Emission Tomography,PET)技术在实体肿瘤的定性诊断和病灶转移的检查中具有举足轻重的作用,因此非常有必要提高PET的成像质量。然而,已有的迭代重建算法基本上都严重依赖于PET的线性模型。考虑到探测器效率、探测系统的几何尺寸、生物组织对光子的衰减以及散射效应等诸多物理因素,该模型无法真实地刻画示踪剂与正弦图数据之间的复杂关系。文中首先提出了一种新的观测模型,通过在原来的线性模型中引入未知输入项来刻画示踪剂与正弦图数据之间的关系。该项由两部分组成:1)系数矩阵,用于进一步描述投影的线性部分;2)未知输入,用于刻画示踪剂的浓度分布和投影数据之间的一些非线性关系。在此新模型的基础上,PET图像重构问题被转化成一个线性无偏的最优估计问题。然后,给出了具有待定增益的线性迭代估计模型,通过将正弦数据向未知输入项的系数矩阵的零空间零域上进行投影,消除了未知输入给线性最优估计带来的困难,借助卡尔曼滤波的设计思路,推导出了前述的估计增益。基于此估计模型,提出了一种基于无偏线性最优估计的重建算法。最后,通过仿真实验,将所提重建算法与期望极大估计算法(Expectation-Maximization reconstruction,EM)、核化的EM算法(Kernel method,KEM)以及基于标准卡尔曼滤波(Kalman Filtering method,KF)的重建算法从均方误差(Mean Square Error,MSE)、信噪比(Signal-Noise-Rate,SNR)两个方面进行了比较。实验结果表明:与其他3种算法相比,所提算法重建的图像具有更大的信噪比、更小的均方误差,视觉上更加清晰,更好地重建了肿瘤的形状和尺寸,因此具有更好的重构质量。     

MIMO-OFDM系统中一种改进的最大似然信道估计算法*

对MIMO-OFDM系统中的信道估计进行研究,提出了一种对最大似然（ML）算法的改进,该算法首先采用ML算法获得初始估计值,然后联合检测进行迭代信道估计,充分利用上了接收端联合数据检测得到的数据信号信息与信道估计进行信息交互来提高估计性能,仿真结果表明,相对于传统估计方法,这种改进方法能够得到更好的均方误差和误码率性能,尤其是在导频数量较少时,此改进算法的性能提升将更明显。     

基于决策树自标识的主动配电网状态估计算法

本文为解决目前配电网前端数据数量大、缺省多、分析复杂等问题,提出一种适用于主动配电网的状态估计算法来管理分析前端数据。本文提出了基于决策树自标识的主动配电网状态估计算法,通过估计前预处理数据,对数据进行分类以及修正,使输入状态估计模型中的数据有更好的相容性。同时,本文针对分布式能源配套量测装置少的问题,建立了考虑分布式电源的状态估计模型,对分布式能源缺省数据进行补全修正,提高输入数据的质量。该方法运用到实际算例中可以看出,对比传统的状态估计,基于决策树自标识的主动配电网状态估计算法有更好的估计效果以及更快的迭代速度。因此本文提出的算法能有效的运用到当前大规模分布式能源接入的配电网状态估计中。     

Moving data window gradient‐based iterative algorithm of combined parameter and state estimation for bilinear systems

The combined iterative parameter and state estimation problem is considered for bilinear state‐space systems with moving average noise in this paper. There are the product terms of state variables and control variables in bilinear systems, which makes it difficult for the parameter and state estimation. By designing a bilinear state estimator based on the Kalman filtering, the states are estimated using the input‐output data. Furthermore, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. A state estimator‐based MDW gradient‐based iterative (MDW‐GI) algorithm is proposed to estimate the unknown states and parameters jointly. Moreover, given the extended gradient‐based iterative (EGI) algorithm as a comparison, the MDW‐GI algorithm can reduce the impact of noise to parameter estimation and improve the parameter estimation accuracy. The numerical simulation examples validate the effectiveness of the proposed algorithm.     

Simultaneous estimation of the input and output frequencies of nonlinear systems

In this paper, the simultaneous estimation of the input and output frequencies of nonlinear systems is considered. As the output frequencies are generated from the input frequencies, and are integer combinations of these frequencies, it is shown in this paper that the simultaneous estimation of both the input and output frequencies can therefore be formulated as a constrained estimation problem. First, the constrained Cramér-Rao lower bound, an important general property of any unbiased estimator, is derived. The procedure and algorithm for estimating the input and output frequencies are devised based on the periodogram method. Numerical examples are presented to illustrate the performance and implementation of the proposed estimation procedure and algorithm.