Parameter estimation for dual-rate sampled Hammerstein systems with dead-zone nonlinearity

The identification of nonlinear systems with multiple sampled rates is a difficult task.The motivation of our paper is to study the parameter estimation problem of Hammerstein systems with dead-zone characteristics by using the dual-rate sampled data.Firstly,the auxiliary model identification principle is used to estimate the unmeasurable variables,and the recursive estimation algorithm is proposed to identify the parameters of the static nonlinear model with the dead-zone function and the parameters of the dynamic linear system model.Then,the convergence of the proposed identification algorithm is analyzed by using the martingale convergence theorem.It is proved theoretically that the estimated parameters can converge to the real values under the condition of continuous excitation.Finally,the validity of the proposed algorithm is proved by the identification of the dual-rate sampled nonlinear systems.     

FDA-MIMO雷达目标距离、角度及未知互耦参数估计

针对频控阵-多输入多输(frequency diverse array multiple input multiple output, FDA-MIMO)雷达阵列间存在未知互耦合情况的参数估计问题, 提出一种基于数据转换的二维多重信号分类方法。首先, 构造了存在未知互耦合影响的接收信号模型。然后，通过子空间分解的方法得到用于参数估计的谱函数, 并且采用数据转换的方法来解决谱函数由于未知耦合矩阵存在而失真的问题。最后，利用得到的目标距离和角度参数的估计值, 通过类似线性约束最小方差重构优化问题, 估计出耦合系数矩阵。仿真结果验证了所提方法的有效性，能准确估计阵列间存在未知互耦合效应时的参数和互耦合系数。     

基于神经网络的反步自适应大机动飞行控制

针对飞机大机动飞行时模型非线性和参数不确定性的特点,提出了一种基于全调节神经网络的反步自适应控制方法。飞机模型不确定部分由全调节径向基函数(radical basis function, RBF)神经网络在线补偿,控制律及神经网络参数自适应律由反步法回馈递推得到,并利用一种自适应参数策略的混沌粒子群算法优化控制器固定参数,改善动态性能,最后通过加权伪逆控制分配方法得到最终控制信号。仿真结果表明：在较大的模型气动参数不确定及控制增益矩阵未知时,所设计的控制律仍能理想地跟踪飞机大机动指令飞行,神经网络参数估计误差指数收敛到有界紧集,系统具有快速的收敛性和良好的鲁棒性。     

Signal estimation with binary-valued sensors

<正> This paper introduces several algorithms for signal estimation using binary-valued outputsensing.The main idea is derived from the empirical measure approach for quantized identification,which has been shown to be convergent and asymptotically efficient when the unknown parametersare constants.Signal estimation under binary-valued observations must take into consideration oftime varying variables.Typical empirical measure based algorithms are modified with exponentialweighting and threshold adaptation to accommodate time-varying natures of the signals.Without anyinformation on signal generators,the authors establish estimation algorithms,interaction between noisereduction by averaging and signal tracking,convergence rates,and asymptotic efficiency.A thresholdadaptation algorithm is introduced.Its convergence and convergence rates are analyzed by using theODE method for stochastic approximation problems.     

基于串联型扩张状态观测器的参数辨识

文[1]利用扩张状态观测器辩识系统参数，对于低阶系数取得了较好的辨识效果。但用此方法进行高阶系统的参数辨识时，观测器参数不易调整并且很难在一次辨识中取得较高的辨识精度。为了克服上述方法的两上主要弱点，本文利用串联型扩张状态观测器的输出提供的信息及参数辨识的逐次求精法，逐步缩小系统的不确定范围，提高观测器的估计精度，从而也提高了利用观测器输出来进行参数辨识的精度。同时，此方法也大大降低了调整观测器参数的难度。     

Differentially Private Distributed Parameter Estimation

Data privacy is an important issue in control systems, especially when datasets contain sensitive information about individuals. In this paper, the authors are concerned with the differentially private distributed parameter estimation problem, that is, we estimate an unknown parameter while protecting the sensitive information of each agent. First, the authors propose a distributed stochastic approximation estimation algorithm in the form of the differentially private consensus+innovations(DP-CI...     

SIMO系统辅助变量最小二乘盲辨识方法

辅助变量辨识方法是一类重要的辨识方法,然而对于盲辨识,系统输入未知,辅助矩阵的选择就成了难题。针对盲辨识领域研究最多的单输入多输出(SIMO)系统,利用辅助变量方法研究相应的盲辨识方法,其基本思想是联立其中两个子系统进行辨识,利用其他子系统的输出来构造辅助矩阵,从而提出了辅助变量最小二乘盲辨识方法,来获得系统参数估计。还给出所提算法的递推形式,并进行了收敛性分析。仿真例子验证了所提方法的有效性。     

一类不确定混沌系统的自适应模糊同步控制

针对一类带有未知非线性函数、参数和外界干扰的混沌系统,通过将一个时变参数引入到带有非线性后件的T-S模糊逻辑系统中,结合自适应方法对未知参数进行在线估计,完成了自适应模糊同步控制器的设计,并实现了驱动-响应混沌系统的渐近同步。一般而言,带有非线性后件的T-S模糊逻辑系统具有更高的逼近能力,可用更少的规则去逼近主从系统中的未知非线性函数,且在同步控制器的设计过程中,参数自适应律的个数与模糊规则的个数无关。因此,该同步方法不仅减少了在线运算量,而且通过直觉推理生成规则少、解释性强的模糊逻辑系统具有更广泛的应用。最后所给数值仿真算例说明了该方法的有效性。     

基于改进核平滑辅助粒子滤波的失效预测方法

针对系统模型存在多个未知参数的情况,提出了一种基于改进核平滑辅助粒子滤波(improved kernel smoothing auxiliary particle filtering, IKS-APF)的失效预测方法。首先,在已有核平滑辅助粒子滤波基础上引入增益因子和加速因子,使其具有参数方差双向调节能力和更快的参数估计收敛速度。然后,使用ISK-APF进行状态和参数的联合估计,为确保参数估计的准确性同时减少参数的不确定性,设计了方差监视和短期预测误差匹配相结合的自适应粒子方差控制方案。最后,使用最新估计到的状态和参数粒子进行迭代预测,并通过统计状态粒子首达失效状态空间的时间计算出剩余使用寿命（remaining useful life, RUL）。仿真结果证明了本文方法的有效性和优越性。     

FIR systems identification under quantized output observations and a large class of persistently exciting quantized inputs

This paper investigates the FIR systems identification with quantized output observations and a large class of quantized inputs. The limit inferior of the regressors' frequencies of occurrences is employed to characterize the input's persistent excitation, under which the strong convergence and the convergence rate of the two-step estimation algorithm are given. As for the asymptotical efficiency,with a suitable selection of the weighting matrix in the algorithm, even though the limit of the product of the Cram′er-Rao(CR) lower bound and the data length does not exist as the data length goes to infinity, the estimates still can be asymptotically efficient in the sense of CR lower bound. A numerical example is given to demonstrate the effectiveness and the asymptotic efficiency of the algorithm.     

Identification of multiple inputs single output errors-in-variables system using cumulant

A higher-order cumulant-based weighted least square（HOCWLS） and a higher-order cumulant-based iterative least square（HOCILS） are derived for multiple inputs single output（MISO） errors-in-variables（EIV） systems from noisy input/output data. Whether the noises of the input/output of the system are white or colored, the proposed algorithms can be insensitive to these noises and yield unbiased estimates. To realize adaptive parameter estimates, a higher-order cumulant-based recursive least square（HOCRLS） method is also studied. Convergence analysis of the HOCRLS is conducted by using the stochastic process theory and the stochastic martingale theory. It indicates that the parameter estimation error of HOCRLS consistently converges to zero under a generalized persistent excitation condition. The usefulness of the proposed algorithms is assessed through numerical simulations.     

基于一种增量式一元线性回归模型的自适应逆控制

为受控系统提出了一种简便实用的增量式一元线性回归模型。为了跟踪快时变参数 ,提出了一种滚动多模型加权平均参数估计算法。在参数估计和系统控制的过程中运用智能技术 ,使估值更可靠 ,并形成了以基于该模型的自适应逆控制为主、常规控制为辅的多模态控制方式。仿真结果表明 ,这一控制方式对于控制非线性和时变系统非常有效。     

An M-Objective Penalty Function Algorithm Under Big Penalty Parameters

Some classical penalty function algorithms may not always be convergent under big penalty parameters in Matlab software, which makes them impossible to find out an optimal solution to constrained optimization problems. In this paper, a novel penalty function (called M-objective penalty function) with one penalty parameter added to both objective and constrained functions of inequality constrained optimization problems is proposed. Based on the M-objective penalty function, an algorithm is developed to solve an optimal solution to the inequality constrained optimization problems, with its convergence proved under some conditions. Furthermore, numerical results show that the proposed algorithm has a much better convergence than the classical penalty function algorithms under big penalty parameters, and is efficient in choosing a penalty parameter in a large range in Matlab software.     

PARAMETER ESTIMATION METHODOLOGY FOR NONLINEAR SYSTEMS： APPLICATION TO INDUCTION MOTOR

1. Introduction In recent years, there has been a lot of research concerning parameter estimation (Akatsu and Kawamura 2000, LandauAnderson and De Bruyne 2000, Marino Peresada and Tomei 2000, Floret and Lamnabhi-Lagarrigue 2001, Pavlov and Zaremba 2001, Floret 2002, Kenné 2003). In linear systems and in some specific nonlinear cases, parameter estimation is performed using the least square algorithm (Walter and Pronzato 1994, Landau 1998). The application of this technique in the case…     

一种有效的参数估计方法在预缩聚反应中应用

采用改进的遗传算法解决复杂聚合反应模型的参数估计问题.算法采用排序选择、多点交叉和变异优选策略,有效地提高遗传算法的搜索性能,避免了序贯优化方法有可能存在局部极值的问题.根据文献数据,仿真结果表明,该算法在参数估计中,具有参数搜索范围大、收敛速度快和精度高等特点,它能够有效地解决非线性参数估计问题.     

混沌系统的参数辨识与控制

把观测器思想用于系统中未知参数的辨识 ,对混沌系统中的未知参数进行了辨识研究。数值结果表明 ,对未知参数为常数或缓慢变化的信号 ,提出的方法都能给出很好的辨识结果。随后 ,把辨识和控制问题综合起来考虑 ,提出改进的混沌控制方法 ,数值仿真表明了该方法的有效性     

修正的最小二乘拟合波达方向估计方法

首先分析了基于最小二乘拟合技术的多普勒信号波达方向估计方法原理 ,在此基础上 ,进一步讨论了采样时间相关矩阵发生亏秩对该方法的影响 ,即由于加权矢量的可选择自由度过大导致最优化问题出现病态。但可以证明 ,所有满足约束条件的加权矢量均可使最小二乘拟合问题收敛至相同的极值点 ,所以选择具有最小范数的加权矢量最优解是合理的。此外 ,对原问题进行降秩或秩恢复处理也可得到满意的收敛结果 ,文中对此作了详细讨论并给出了相应的修正步骤。最后给出的计算机仿真结果验证了所给修正方法的有效性。     

ADAPTIVE SYSTEMS THEORY： SOME BASIC CONCEPTS, METHODS AND RESULTS

The adaptive systems theory to be presented in this paper consists of two closely related parts: adaptive estimation (or filtering, prediction) and adaptive control of dynamical systems. Both adaptive estimation and control are nonlinear mappings of the on-line observed signals of dynamical systems, where the main features are the uncertain-ties in both the system‘s structure and external disturbances, and the non-stationarity and dependency of the system signals. Thus, a key difficulty in establishing a mathematical theory of adaptive systems lies in how to deal with complicated nonlinear stochastic dynamical systems which describe the adaptation processes. In this paper, we will illustrate some of the basic concepts, methods and results through some simple examples. The following fundamental questions will be discussed: How much information is needed for estimation? How to deal with uncertainty by adaptation? How to analyze an adaptive system? What are the convergence or tracking performances of adaptation? How to find the proper rate of adaptation in some sense? We will also explore the following more fundamental questions: How much uncertainty can be dealt with by adaptation ? What are the limitations of adaptation ? How does the performance of adaptation depend on the prior information ? We will partially answer these questions by finding some “critical values“ and establishing some “Impossibility Theorems“ for the capability of adaptation, for several basic classes of nonlinear dynamical control systems with either parametric or nonparametric uncertainties.     

基于粒子群优化的时变系统辨识

提出了一种基于粒子群优化的时变系统辨识方法。其基本思想是将时变系统的辨识问题转化为非线性连续函数的在线优化问题 ,然后利用粒子群优化获得系统参数的最优估计。仿真结果显示 ,该方法对于时变参数具有很强的跟踪能力 ,与采用遗传算法的系统辨识方法相比 ,有实现简单、运算量小等特点。     

利用改进SA算法估计河流水质参数的仿真实验

将改进模拟退火法应用于求解分析河流水团示踪试验数据，确定河流水质参数的函数优化问题。针对标准SA算法收敛速度缓慢的弱点，采取了增加附加约束条件、设置内阈值提前降温和增加记忆功能等措施对算法进行了改进。仿真实验结果表明：1)改进措施能够明显地提高算法收敛速度，并可得到满意的参数计算结果；2)内循环次数不会对外循环次数产生明显的影响；3）内阈值的设置对算法的收敛速度影响非常明显，当其值与外阈值接近或相等时，算法的收敛性最佳：4)在研究的具体问题情况下，降温指数不宜过大，其在0．4—0.65之间取值为宜。