基于时延估计的扰动卡尔曼滤波器外力估计

针对机械臂末端力估计存在模型误差及系统扰动等问题,提出了一种基于时延估计的扰动卡尔曼滤波器外力估计法。在不使用额外力传感器的情况下,通过电机驱动电流的测量,采用时间延时估计法设计出不需要精确动力学模型的估计器。将外力作为状态变量,对系统的不确定性和扰动进行观测,考虑机械臂动力学和扰动动力学,设计出扰动卡尔曼滤波器来估计末端所受外力。使用Matlab仿真软件验证了该方法的有效性,结果表明所提估计方法对具有测量噪声、模型误差及不确定性扰动的系统具有很好的鲁棒性。     

Reduced order state estimators for discrete-time stochastic systems

A reduced order, least squares, state estimator is developed for linear discrete-time systems having both input disturbance noise and output measurement noise with no output being free of measurement noise. The order reduction is achieved by using a Luenberger observer in connection with some of the system outputs and a Kalman filter to estimate the state of the Luenberger observer. The order of the resulting state estimator is reduced from the order of the usual Kalman filter system state estimator by the number of system outputs selected for use as inputs to the Luenberger Observer. The manner in which the noise associated with the selected system outputs affects the state estimation error covariance provides considerable insight into the compromise being attempted.     

New Kalman filter and smoother consistency tests

We derive three new tests that can be applied to a Kalman filter to check for inconsistencies. The Filter Residual Test can detect observations that are outliers but would be missed by a basic residual test because the uncertainty of the expected observation is large relative to the uncertainty of the observation. The Smoother Residual Test uses the output from a Modified Bryson–Frazier (MBF) smoother to detect observations that are outliers. The Smoother State Test compares the state estimates from the filter and MBF smoother to detect model inconsistencies, in particular insufficient process noise.     

A treatment of nonwhite measurement noise in discrete linear systems

A combination of pure colored and pure white measurement noise may occur in several classes of linear discrete time dynamic stochastic systems of interest. Accurate state estimation in the presence of this nonwhite measurement noise is achieved through the use of a low order least squares filter. Previously the statistical nature of this nonwhite noise has been restricted, and computationally burdensome algorithms have been developed to handle this particular type of measurement noise. In overcoming these difficulties a filter is developed, similar in form to a Kalman filter, whose order is no higher than the order of the state.     

一阶线性自抗扰控制的整定

线性自抗扰控制（linear active disturbance rejection control,LADRC）是解决系统外部不可测扰动和内部未知不确定性的一种新型控制方法。其精髓是将系统的不确定性转化为一个可观测的状态,利用扩张状态观测器进行实时估计,并用状态反馈控制率实时进行补偿。在满足鲁棒度策略和时间乘平方误差积分的约束条件下,首先针对一阶惯性加迟延模型提出了一阶LADRC的整定公式,然后通过典型的基准系统和温度控制实验,对整定公式进行测试,最后与常规的SIMC （simplified internal model control）-PI （proportional-integral）整定方法进行性能比较。仿真结果证明了该一阶LADRC整定公式的可行性,拓展了其在工业控制领域的应用。     

Kalman filter based fault diagnosis of networked cont rol system with white noise

The networked control system NCS is regarded as a sampled control system with output time- variant delay. White noise is considered in the model construction of NCS. By using the Kalman filter theory to compute the filter parameters ,a Kalman filter is constructed for this NCS. By comparing the output of the filter and the practical system ,a residual is generated to diagnose the sensor faults and the actuator faults. Finally ,an example is given to show the feasibility of the approach.     

Kalman filter based fault diagnosis of networked control system with white noise

The networked control system NCS is regarded as a sampled control system with output time-variant delay. White noise is considered in the model construction of NCS. By using the Kalman filter theory to compute the filter parameters, a Kalman filter is constructed for this NCS.By comparing the output of the filter and the practical system,a residual is generated to diagnose the sensor faults and the actuator faults. Finally, an example is given to show the feasibility of the approach.     

Stability of the Kalman filter for continuous time output error systems

The stability of the Kalman filter is usually ensured by the uniform complete controllability regarding the process noise and the uniform complete observability of linear time varying systems. This paper studies the case of continuous time output error systems, in which the process noise is totally absent. The classical stability analysis assuming the controllability regarding the process noise is thus not applicable. It is shown in this paper that the uniform complete observability alone is sufficient to ensure the asymptotic stability of the Kalman filter applied to time varying output error systems, regardless of the stability of the considered systems themselves. The exponential or polynomial convergence of the Kalman filter is then further analyzed for particular cases of stable or unstable output error systems.     

Robust speech recognition system using multi-parameter bidirectional Kalman filter

A speech signal processing system using multi-parameter model bidirectional Kalman filter has been proposed in this paper. Conventional unidirectional Kalman filter usually performs estimation of current state speech signal by processing the time varying autoregressive model of speech signals from the past time states. A bidirectional Kalman filter utilizes the past and future measurements to estimate the current state of a speech signal that minimize the mean of the squared error using efficient recursive means. The matrices involved in the difference equations and the measurement equations of the bidirectional Kalman filter algorithm are kept constant throughout the process. With multi-parameter model, the proposed bidirectional Kalman filter relates more measurements from the future and past time states to the current time state. The proposed multi-parameter bidirectional Kalman filter has been implemented into a speech recognition system and its performance has been compared to other conventional speech processing algorithms. Compared to the single-parameter model bidirectional Kalman filter, the multi-parameter bidirectional Kalman filter improves the accuracy in the state prediction, reduces the speech information lost after the filtering process and better word error rate has been achieved at high SNR regions (clean, 20, 15, 10 dB).     

模糊自适应卡尔曼滤波算法在航位推算系统中的应用

针对实际的应用中车载航位推算系统的模型参数、噪声的统计特性不确定性,影响估计效果,提出了车载航位推算的模糊自适应卡尔曼滤波模型及其滤波算法;该方法通过监视理论残差与实际残差的比值是否接近1,应用模糊推理系统不断地调整量测噪声协方差的加权,对自适应卡尔曼滤波的量测噪声协方差进行递推修正,通过该算法来抑制噪声对精度的影响,进而提高系统的导航精度;仿真结果表明,这种算法能够有效地提高系统的精度,是一种比较理想的车载DR导航滤波方法。     

Wiener theory of digital linear-quadratic control

The linear quadratic regulator problem is considered for discrete time systems with time delay. The theory forms a close parallel to that for stationary prediction and filtering. For coloured noise disturbance at the plant input it is shown that the optimal control decomposes into the solution for white noise disturbance plus a feedforward controller. For unit time delay the solution becomes equivalent to that given by the stationary Kalman theory.     

基于状态集员估计的主动故障检测

对于现代复杂控制系统, 微小故障往往很难发现. 在系统过程干扰和测量噪声未知但有界的前提下, 提出了一种新的基于状态集员估计的主动故障检测方法. 首先设计全对称多胞形卡尔曼滤波器对系统状态进行估计, 并利用全对称多胞形对受未知干扰影响的状态集合进行描述, 然后设计辅助输入信号使得加入辅助输入信号后正常模型的状态集合与故障模型的状态集合交集为空, 从而实现主动故障检测. 为了使得所设计的辅助输入信号对原系统影响最小, 需要求得最小的辅助输入信号, 本文将最优化问题转化为混合整数二次规划问题进行求解. 最后, 与基于输出集合的辅助输入信号设计方法对比, 仿真验证本文所提出的基于状态集合的主动故障检测方法由于未受下一时刻测量噪声的影响, 所求得的辅助输入信号更小, 保守性更低.     

含时延和丢失数据不确定系统的FIR滤波器

针对状态空间模型中存在服从伯努利分布的时延和随机观测丢失的情况,基于极大似然法则,分别设计有限脉冲响应(finite impulse response, FIR)滤波器的慢速率批处理形式和快速率迭代形式.首先,将时延和数据丢失情况下的模型表述为服从伯努利分布的概率线性函数;然后,通过极大似然处理从而得到所提出极大似然FIR算法;最后,将在相同条件下的极大似然FIR估计、改进型卡尔曼滤波以及无偏FIR估计3种滤波方法进行对比,从估计误差、均方根误差和不确定性影响等角度进行比较分析.实验部分通过3-DOF直升机模型仿真,可发现所提出极大似然FIR估计方法在处理时延和数据丢失问题时更加有效,鲁棒性更高.     

基于鲁棒H∞滤波的蓄电池荷电状态估计

针对蓄电池系统的荷电状态(SOC)受蓄电池材料及加工制作、工作温度、充放电大小及频率等因素的影响,是一个典型的非线性时变系统,相应的状态估计模型在测量过程中存在噪声干扰引起模型参数不确定性的特征。以安时法为基础,建立SOC的状态方程并应用鲁棒H∞滤波算法预测SOC估计值。仿真研究表明,提出的鲁棒H∞滤波算法在有色噪声干扰下比卡尔曼滤波(Kalman filter)有更好的估计精度;在白噪声情况下,鲁棒H∞滤波算法可通过调节其参数达到和卡尔曼滤波器相同的估计精度。     

有色量测噪声下的HCKF及其应用

针对高阶容积卡尔曼滤波（HCKF）算法在有色量测噪声条件下滤波精度下降的问题,提出了有色量测噪声下的HCKF算法。通过一阶马尔科夫模型将有色量测噪声进行白化,将带有色量测噪声的非线性离散随机系统转化为白噪声下的非线性时滞系统,并给出高斯域内针对非线性时滞系统的贝叶斯滤波框架。利用高阶容积准则对该滤波框架进行近似计算,进而得到有色量测噪声下的HCKF算法。将所提算法应用到机动目标跟踪系统中,仿真实验结果表明,量测噪声为白噪声时,所提算法与标准HCKF算法具有相同的估计性能;在量测噪声为有色噪声时,所提算法相比于标准HCKF具有更优的估计精度和鲁棒性。     

基于新型卡尔曼滤波算法的称重系统实现研究

针对信号采集中存在脉冲干扰和高斯白噪声引起的误差问题,提出了基于新型卡尔曼滤波算法的称重系统实现方案,给出了该称重系统的组成。该算法对信号进行阈值和防脉冲干扰滤波,在防脉冲干扰滤波过程中不断优化阈值滤波中的对比值,消除了干扰幅度较大的非周期脉冲信号;将信号送入下一级滤波器进行时间更新、测量更新,消除了高斯白噪声干扰。一系列试验表明,测量范围内系统灵敏度约为0.15 mV/kg;最大差值为0.1 kg,准确度达到99.7%;经过新型卡尔曼滤波后误差均处于0.4%以下,相比使用传统的卡尔曼滤波算法,系统精度提高到4.5倍。     

A comparison of nonlinear observers for output feedback model-based control of seeded batch crystallization processes

This paper presents a comparative analysis of various nonlinear estimation techniques when applied for output feedback model-based control of batch crystallization processes. Several nonlinear observers, namely an extended Luenberger observer, an extended Kalman filter, an unscented Kalman filter, an ensemble Kalman filer and a moving horizon estimator are used for closed-loop control of a semi-industrial fed-batch crystallizer. The performance of the nonlinear observers is evaluated in terms of their closed-loop behavior as well as their ability to cope with model imperfections and process uncertainties such as measurement errors and uncertain initial conditions. The simulation results suggest that the extended Kalman filter and the unscented Kalman filter provide accurate state estimates that ensure adequate fulfillment of the control objective. The results also confirm that adopting a time-varying process noise covariance matrix further enhances the estimation accuracy of the latter observers at the expense of a slight increase in their computational burden. This tuning method is particularly suited for batch processes as the state variables often vary significantly along the batch run. It is observed that model imperfections and process uncertainties are largely detrimental to the accuracy of state estimates. The degradation in the closed-loop control performance arisen from inadequate state estimation is effectively suppressed by the inclusion of a disturbance model into the observers.     

基于鲁棒自适应Kalman滤波的PET放射性浓度重建

针对正电子发射断层成像重建过程中存在的系统模型误差和投影数据不确定性,提出了基于状态空间体系的鲁棒自适应Kalman滤波法。该方法根据药物动力学先验信息建立状态方程,结合PET测量方程组成状态空间模型。引入虚拟噪声来表示模型的系统矩阵误差之后,通过应用鲁棒自适应Kalman滤波法对未知的系统噪声以及观测噪声进行估计的同时完成PET放射性浓度的重建。实验结果表明,此算法比传统的最大似然法和滤波反投影法更具鲁棒性,适合应用于实际PET系统中。