An Analytic Geometry Approach to Wiener System Frequency Identification

This paper addresses the problem of Wiener system identification. The underlying linear subsystem is stable but not necessarily parametric. The nonlinear element in turn is allowed to be nonparametric, noninvertible, and nonsmooth. As Wiener models are uniquely defined up to an uncertain multiplicative factor, it makes sense to start the frequency identification process estimating the system phase (which is common to all models). To this end, a consistent estimator is designed using analytic geometry tools. Accordingly, the system frequency behavior is characterized by a family of Lissajous curves. Interestingly, all these curves are candidates to modelling the system nonlinearity, but the most convenient one is the less spread of them. Finally, the frequency gain is in turn consistently estimated optimizing an appropriate cost function involving the obtained phase and nonlinearity estimates.     

基于扰动和摩擦补偿的柔性机械臂系统非奇异快速终端滑模控制

本文针对系统中存在的关节摩擦、动力学参数不确定性和外部负载干扰等因素引起的柔性机械臂系统控制性能下降的问题,提出了一种基于扰动和摩擦补偿的非奇异快速终端滑模控制方法(NFTSMC-DE-FC).首先,设计扰动估计器(DE)对系统未知动态参数和负载干扰进行估计.然后,针对扰动估计器不能精确估计的关节摩擦力矩进行辨识.最后,利用滑模控制技术设计非奇异快速终端滑模控制器,并将扰动估计值和摩擦力辨识值以前馈的方式进行补偿,实现对柔性机械臂系统给定参考轨迹跟踪的准确性以及对外界扰动的鲁棒性.值得注意的是,与传统只使用扰动估计器的方法相比,本文考虑到了摩擦力等非线性因素的影响,并利用辨识技术对摩擦力进行辨识,提高了控制精度.利用Lyapunov稳定性定理从理论上证明了所设计的控制器可以保证闭环系统的稳定性.实验结果表明,相较于非奇异快速终端滑模控制方法(NFTSMC)和基于扰动估计器的非奇异快速终端滑模控制方法(NFTSMC-DE),所提方法提高了柔性机械臂系统的轨迹跟踪性能.     

Trading variance reduction with unbiasedness: the regularized subspace information criterion for robust model selection in kernel regression

A well-known result by Stein (1956) shows that in particular situations, biased estimators can yield better parameter estimates than their generally preferred unbiased counterparts. This letter follows the same spirit, as we will stabilize the unbiased generalization error estimates by regularization and finally obtain more robust model selection criteria for learning. We trade a small bias against a larger variance reduction, which has the beneficial effect of being more precise on a single training set. We focus on the subspace information criterion (SIC), which is an unbiased estimator of the expected generalization error measured by the reproducing kernel Hilbert space norm. SIC can be applied to the kernel regression, and it was shown in earlier experiments that a small regularization of SIC has a stabilization effect. However, it remained open how to appropriately determine the degree of regularization in SIC. In this article, we derive an unbiased estimator of the expected squared error, between SIC and the expected generalization error and propose determining the degree of regularization of SIC such that the estimator of the expected squared error is minimized. Computer simulations with artificial and real data sets illustrate that the proposed method works effectively for improving the precision of SIC, especially in the high-noise-level cases. We furthermore compare the proposed method to the original SIC, the cross-validation, and an empirical Bayesian method in ridge parameter selection, with good results.     

A New Approach To Optimal and Self‐Tuning Filtering,Smoothing and Prediction For Discrete‐Time Systems

A new approach to optimal and self‐tuning state estimation of linear discrete time‐invariant systems is presented, using projection theory and innovation analysis method in time domain. The optimal estimators are calculated by means of spectral factorization. The filter, predictor, and smoother are given in a unified form. Comparisons are made to the previously known techniques such as the Kalman filtering and the polynomial method initiated by Kucera. When the noise covariance matrices are not available, self‐tuning estimators are obtained through the identification of an ARMA innovation model. The self‐tuning estimator asymptotically converges to the optimal estimator.     

Guest Editorial

The identification of continuous time models from non-uniformly sampled data records is investigated and a new identification algorithm based on the state variable filter approach is derived. It is shown that the orthogonal least squares estimator can be adapted for the identification of continuous time models from non-uniformly sampled data records and instrumental variables are introduced to reduce the bias in stochastic system identification. Multiplying the filtered variables obtained from the state variable filter, with higher powers of the noise free output signal prior to the estimation, is shown to enhance the parameter estimates. Simulated examples are included to illustrate the models.     

Distributed fault‐tolerant control design for spacecraft finite‐time attitude synchronization

This paper develops two distributed finite‐time fault‐tolerant control algorithms for attitude synchronization of multiple spacecraft with a dynamic virtual leader in the presence of modeling uncertainties, external disturbances, and actuator faults. The leader gives commands only to a subset of the followers, and the communication flow between followers is directed. By employing a novel distributed nonsingular fast terminal sliding mode and adaptive mechanism, a distributed finite‐time fault‐tolerant control law is proposed to guarantee all the follower spacecraft that finite‐time track a dynamic virtual leader. Then utilizing three distributed finite‐time sliding mode estimators, an estimator‐based distributed finite‐time fault‐tolerant control law is proposed using only the followers' estimates of the virtual leader. Both of them do not require online identification of the actuator faults and provide robustness, finite‐time convergence, fault‐tolerant, disturbance rejection, and high control precision. Finally, numerical simulations are presented to evaluate the theoretical results. Copyright © 2015 John Wiley & Sons, Ltd.     

Geometrical Parameter Estimation for Industrial Manipulators Using Two-step Estimation Schemes

This paper proposes a new solution for estimating the kinematic parameters of a manipulator, using a two-step linear estimator. The estimator is based on the recursive least-squares (RLS) method. The main characteristics of the method are that one singularity in estimation is overcome and parameters estimates approach the true values more quickly and smoothly than for algorithms using a one-step estimator. The algorithm can be applied for the estimation of parameters of both serial-link robots and robots having a kinematic closed-loop. Simulation results are given, comparing the proposed method with conventional approaches.     

Robust Kalman filter and smoother for errors‐in‐variables state space models with observation outliers based on the minimum‐covariance determinant estimator

In this paper, we propose a robust Kalman filter and smoother for the errors‐in‐variables (EIV) state space models subject to observation noise with outliers. We introduce the EIV problem with outliers and then present the minimum covariance determinant (MCD) estimator which is a highly robust estimator in terms of protecting the estimate from the outliers. Then, we propose the randomized algorithm to find the MCD estimate. However, the uniform sampling method has a high computational cost and may lead to biased estimates, therefore we apply the sub‐sampling method. A Monte Carlo simulation result shows the efficiency of the proposed algorithm. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Identification of structural systems using naturally induced vibration data in the presence of measurement noise

The identification of structural systems using naturally induced vibration data in the presence of measurement noise by the method of instrumental variables is studied. It is well known that when measurement noise is present least squares methods will yield an inconsistent estimator. This leads us to consider methods which will yield a consistent estimator in the presence of noise. The maximum likelihood method provides a solution to the method, but is difficult to implement in the case of large systems because of the amount of computation required. In this paper we present the application of the method of instrument variables for the identification of the parameters of structural systems excited by white noise in the presence of white measurement noise. The equations required for the application of the method to a structural system and the resulting consistent estimator are derived. Although the concept of instrumental variables is not new, the application of this method to problems of structural systems is sufficiently attractive to justify its presentation. The results of simulation experiments which verify the theoretical development are presented.     

Multisensor fusion fault tolerant control

In this paper, a multisensor fusion fault tolerant control system with fault detection and identification via set separation is presented. The fault detection and identification unit verifies that for each sensor–estimator combination, the estimation tracking errors lie inside pre-computed sets and discards faulty sensors when their associated estimation tracking errors leave the sets. An active fault tolerant controller is obtained, where the remaining healthy estimates are combined using a technique based on the optimal fusion criterion in the linear minimum-variance sense. The fused estimates are then used to implement a state feedback tracking controller. We ensure closed-loop stability and performance under the occurrence of abrupt sensor faults. Experimental validation, illustrating the multisensor fusion fault tolerant control strategy is included.     

On the use of marginal posteriors in marginal likelihood estimation via importance sampling

The efficiency of a marginal likelihood estimator where the product of the marginal posterior distributions is used as an importance sampling function is investigated. The approach is generally applicable to multi-block parameter vector settings, does not require additional Markov Chain Monte Carlo (MCMC) sampling and is not dependent on the type of MCMC scheme used to sample from the posterior. The proposed approach is applied to normal regression models, finite normal mixtures and longitudinal Poisson models, and leads to accurate marginal likelihood estimates.     

稳健MM估计在扩散张量成像中的应用

在扩散加权图像中存在由热噪声产生的高斯分布偏差和生理噪声产生的异常点,最小二乘(LS)法对于高斯分布偏差具有较好的估算效果,但是对异常点不稳健。为此,采用稳健MM估计方法对扩散张量成像(DTI)数据进行张量估算,将高失效点算法的估计结果作为初始估计值,进行两步M估计。模拟数据与真实数据的实验结果表明,该估计方法具有较好的稳健性,并能有效估算扩散张量。     

Continuously Differentiable Sample-Spacing Entropy Estimation

The insufficiency of using only second-order statistics and premise of exploiting higher order statistics of the data has been well understood, and more advanced objectives including higher order statistics, especially those stemming from information theory, such as error entropy minimization, are now being studied and applied in many contexts of machine learning and signal processing. In the adaptive system training context, the main drawback of utilizing output error entropy as compared to correlation-estimation-based second-order statistics is the computational load of the entropy estimation, which is usually obtained via a plug-in kernel estimator. Sample-spacing estimates offer computationally inexpensive entropy estimators; however, resulting estimates are not differentiable, hence, not suitable for gradient-based adaptation. In this brief paper, we propose a nonparametric entropy estimator that captures the desirable properties of both approaches. The resulting estimator yields continuously differentiable estimates with a computational complexity at the order of those of the sample-spacing techniques. The proposed estimator is compared with the kernel density estimation (KDE)-based entropy estimator in the supervised neural network training framework with computation time and performance comparisons.      

离散系统状态和参数鲁棒滤波分离算法

提出了一种离散系统的鲁棒分离滤波方法.为了对状态向量进行较准确估计,将鲁棒滤波器分为:1)零误差状态估计器;2)不确定矩阵估计器;3)鲁棒合成器.零偏差状态估计器是假定系统的不确定部分为零时的状态估计器;其新息作为不确定部分的估计变量,并由此估计系统的不确定部分;最后,根据系统不确定部分估计误差的上下界,用鲁棒合成器对状态向量的估计值进行鲁棒修正.为了在合成器中得到鲁棒滤波的逼近计算式,通过变换状态估计误差的协方差阵,得到了系统矩阵不确定部分的误差上界不等式逼近,并且得到了估计误差协方差阵逆阵的下界不等式逼近,从而给出了鲁棒合成滤波的完整算法.     

A nonlinear set‐membership filter for on‐line applications

A guaranteed estimator for a general class of nonlinear systems and on‐line usage is developed and analysed. This filter bounds the linearization error, then applies a linear set‐membership filter such that stability guarantees hold for nonlinear systems. A tight bound on the linearization error is found using interval analysis. This filter recursively estimates an ellipsoidal set in which the true state lies. General assumptions include the use of bounded noises and twice continuously differentiable dynamics. When the system is uniformly observable, it is proven that the nonlinear set‐membership filter is stable. In addition, if no noise is present and the initial error is small, the error between the centre of the estimated set and the true value converges to zero. The result is an estimator which is computationally attractive and can be implemented robustly in real‐time. The proposed method is applied to a two‐state example to demonstrate the theoretical results. Copyright © 2003 John Wiley & Sons, Ltd.     

A robust controller for trajectory tracking of a DC motor pendulum system

This work presents a solution to the output feedback trajectory tracking problem for an uncertain DC motor pendulum system under the effect of an unknown bounded disturbance. The proposed algorithm uses a Proportional Derivative (PD) controller plus a novel on-line estimator of the unknown disturbance. The disturbance estimator is obtained by coupling a standard second-order Luenberger observer with a third-order sliding modes differentiator. The Luenberger observer provides estimates of the motor angular position and velocity. Moreover, an ideal disturbance estimator in terms of the Luenberger observer error and its first and second time derivatives is obtained from the observer error formulae; these time derivatives are not available from measurements. Subsequently, the sliding modes third-order differentiator allows obtaining estimates of these time derivatives in finite time. The estimates replace the real values of the first and second time derivatives in the ideal disturbance estimator thus producing a practical disturbance estimator, and also permit obtaining an estimate of the motor angular velocity. A depart from previous approaches is the fact that the disturbance is not directly estimated by the Luenberger observer or the third-order differentiator. Numerical simulations and real-time experiments validate the effectiveness of the proposed approach.     

Building Sugeno-type models using fuzzy discretization andorthogonal parameter estimation techniques

This paper develops a new approach to building Sugeno-type models. The essential idea is to separate the premise identification from the consequence identification, while these are mutually related in the previous methods. A fuzzy discretization technique is suggested to determine the premise of the model, and an orthogonal estimator is provided to identify the consequence of the model. The orthogonal estimator can provide information about the model structure, or which terms to include in the model, and final parameter estimates in a very simple and efficient manner. The well-known gas furnace data of Box and Jenkins is used to illustrate the proposed modeling approach and to compare its performance with other statistical and fuzzy modeling approaches. It shows that the performance of the new approach compares favorably with these existing techniques     

An efficient method of estimating the true value of a population characteristic from its discrepant estimates

A fruitful method of pooling data from disparate sources, such as a set of sample surveys, is developed. This method proceeds by finding the first two moments of two conditional distributions derived from a joint distribution of two sample estimators of employment for each of several geographical areas. The nature of the two estimators is such that one of them can yield a better estimate of national employment than the other. The regression of the former estimator on the latter estimator with stochastic intercept and slope is used to generate an improved estimator that is equal to bias- and error-corrected estimator for each area with probability 1. This analysis is extended to cases where more than two estimates of employment are available for each area.     

First-order tracking properties of weighted least squaresestimators

The first-order tracking properties (evolution of the mean tracking error) of weighted-least-squares (WLS) estimators applied to nonstationary system identification are investigated. The expected path of the parameter estimates is evaluated. The concept of the impulse response associated with the WLS estimator is introduced     

On output-error methods for system identification

In this paper are derived consistency and asymptotic normality results for the output-error method of system identification. The output-error estimator has the advantage over the prediction-error estimator of being more easily computable. However, it is shown that the output-error estimator can never be more efficient than the prediction-error estimator. The main result of the paper provides necessary and sufficient conditions for the output-error estimator and the prediction-error estimator to have the same efficiency, irrespective of the spectral density of the noise process.