基于有限记忆变遗忘因子的子空间辨识算法

针对传统递推子空间辨识算法对时变参数跟踪速度慢的问题,基于自适应变遗忘因子机制提出一种新的子空间辨识算法.为此首先设计了变遗忘因子作用下输入输出Hankel矩阵的更新机制;然后运用系统矩阵特征值空间欧氏距离信息实现变遗忘因子的自适应更新;最后为隔断历史数据的作用,采用有限记忆法进一步改进算法.理论及仿真结果表明,新算法跟踪速度快、跟踪效果好.     

一种基于变因子梯度型PAST 的递推子空间辨识算法

针对传统递推子空间算法采用固定遗忘因子而存在易受噪声干扰等问题,提出一种新的变因子递推子空间辨识算法.该算法首先引入变因子构造和更新输入输出Hankel矩阵以及观测向量;然后利用变因子改进梯度型子空间跟踪算法估计系统的广义能观测矩阵,并由广义能观测矩阵来估计系统矩阵;最后运用系统矩阵的特征值空间欧氏距离信息实现变因子更新步骤,使算法具有自适应性.将所提出算法应用于一类时不变系统和慢变系统模型,数值仿真结果表明该算法跟踪速度较快且跟踪效果良好.     

基于Givens Rotation正交变换的OBE自适应辨识算法

对应对矩阵反演公式及QR分解定理,加权最小二乘法（WRLS）存在两类不同的算法结构。基于QR分解的结构中,建立于Givens Rotation正交变换的算法优点在于：并行算法的结构易于实时在线计算;采用的登记处融入与剔除形式易于扩展与调整。集元辨识（Set membership）法中的最优界椭球算法（Optimal Bunded ellipsoid,OBE）采用优化策略与有效数据评价准则,实现了冗余数据的过滤并具有较理想的时变参数跟踪潜力。OBE辨识算法与WRLS方法间存在紧密的联系,但在基于矩阵反演公式的加仅最小二乘法上所获得的OBE递推算法结构并不能充分利用其自适应跟踪潜力。本文在Givens Rotation正交变换基础上建立相应的OBE递推算法结构,并充分利用批示时变参数变化的监测因子,进一步引入自适应调整策略。仿真结果显示,该方法具有较佳的时变参数跟踪效果。     

自调整遗忘因子的有色噪声时变系统辨识

基于最小二乘原理,针对含有有色噪声的时变系统,为得到待辨识参数的无偏估计引入辅助变量,同时针对实际工程中的时变系统的动态特性变化复杂性,利用后验误差的加权平方和自动调整遗忘因子.此算法具有较强的实时跟踪能力和较高的估计精度,仿真结果进一步验证了新算法的有效性.     

面向3维空间的移动机器人快速自适应SLAM算法

针对3维空间中移动机器人同时定位与地图构建(SLAM)问题,提出了一种基于改进强跟踪滤波(STF)的快速自适应SLAM算法.该算法首先对于强跟踪滤波器的噪声协方差阵进行在线自适应估计,用于抑制噪声对系统状态估计的影响,使系统状态估计迅速收敛到真实值附近;随后将状态协方差矩阵进行奇异值分解(SVD),提高算法的数值稳定性.该算法可提高对系统时变的自适应能力以及系统状态估计精度.与基于强跟踪滤波器的SLAM算法的仿真对比结果说明了该算法的有效性及其在抑噪性能和估计精度方面的优越性.     

SF与RLS算法结合的时变系统参数辨识

遗忘因子最小二乘算法(RLS)具有对时变系统参数在线估计的能力,而传统的遗忘策略对解决参数辨识矩阵过饱和问题具有一定局限性.为了拓展现有RLS算法在时变系统的适用范围,提出一种将选择遗忘机制(SF)与RLS算法结合的时变系统辨识算法.从而构造出一种基于参数矩阵特征值映射的有界函数,特征值映射函数能够根据系统数据传递过程...     

递推子空间模型辨识算法的研究与仿真

为了很好的解决在线辨识系统模型问题,在对子空间模型辨识研究的基础上,结合递推最小二乘算法和子空问状态辨识方法。推导了子空间状态辨识的递推算法。该算法不仅解决了在线辨识问题,而且算法简单,计算方便,很好地克服了在线辨识时子空间矩阵维数的变化问题。经仿真研究表明,该递推算法克服了一次完成算法在大批量数据运算时,耗时大,专用内存多的缺点,而且对于测量和过程均有噪声干扰的多输入多输出系统,有很好的辨识效果,有较为广阔的应用前景。     

递推子空间自适应预测控制综合方法

为解决预测控制综合方法中的模型不确定问题,不同于以往利用多胞模型描述的方法,提出了一种新的基于递推子空间的自适应预测控制综合方法.通过在每一步中加入当前输入输出数据重新构建Hankel矩阵,对广义能观矩阵进行更新,从而获得对应的状态空间模型;然后将新获得的模型应用于预测综合的优化求解过程,得到当前时刻的控制律.为提高算法的收敛速度,在辨识的过程中引入了基于模型匹配误差的时变遗忘因子.最后,在慢时变与线性时不变两种情况下进行仿真,验证了所提出算法的有效性.     

多元系统耦合带遗忘因子有限数据窗递推最小二乘辨识方法

针对多元线性或非线性回归系统, 将耦合辨识思想与带遗忘因子有限数据窗辨识理论相结合, 提出一种耦合带遗忘因子有限数据窗递推最小二乘辨识算法. 该算法每次递推计算时既不涉及矩阵求逆运算, 又可以克服数据饱和现象, 因此, 该算法不仅计算效率高, 而且可以快速地跟踪时变参数, 获得精确的参数估计. 通过辨识基于多元模型的永磁同步电机参数的实例, 验证了所提出算法的有效性和实用性.

     

多变量系统状态空间模型的递阶辨识

研究多变量系统状态空间模型的递阶辨识问题,推广了作者提出的标量系统状态和参数联合辨识算法.当状态可量测时,利用最小二乘原理直接辨识状态空间模型的参数矩阵;当状态不可测时,利用递阶辨识原理提出了状态空间模型递阶辨识方法,使用系统输入输出数据来估计系统的未知状态和参数.状态空间模型递阶辨识方法分为两步:首先假设系统状态是已知的(即参数估计算法中的未知系统状态用其估计代替),基于状态估计和系统输入输出数据递归计算系统参数估计;然后基于系统输入输出数据和获得的参数估计,递归计算系统的状态估计.     

Recursive subspace identification subject to relatively slow time-varying load disturbance

In this paper, a recursive subspace identification method is proposed to identify linear time-invariant systems subject to load disturbance with relatively slow dynamics. Using the linear superposition principle, the load disturbance response is decomposed from the deterministic-stochastic system response in the form of a time-varying parameter. To ensure unbiased estimation of the deterministic system matrices, a recursive least-squares (RLS) identification algorithm is established with a fixed forgetting factor, while another RLS algorithm with an adaptive forgetting factor is constructed based on the output prediction error to quickly track the time-varying parameter of load disturbance response. By introducing a deadbeat observer to represent the deterministic system response, two extended observer Markov parameter matrices are constructed for recursive estimation. Consequently, the deterministic matrices are retrieved from the identified system Markov parameter matrices. The convergence of the proposed method is analysed with a proof. Two illustrative examples are shown to demonstrate the effectiveness and merit of the proposed identification method.     

基于可变遗忘因子RLS的盲多用户检测

对于直扩码分多址系统,本文提出了一种新的基于可变遗忘因子RLS的自适应盲多用户检测器,它能够自适应地估计检测向量,既具有对时变信道的快速跟踪能力,又具有较小的估计误差。最后通过仿真验证了该方法的有效性,与固定遗忘因子RLS盲多用户检测器相比,新算法具有更高的输出信干比,并且动态环境下的跟踪能力明显提高。另外还研究了参数对性能的影响,为参数的选择作出参考。     

基于正交分解的递推子空间闭环辨识方法

为实现闭环系统在线辨识,提出递推正交分解闭环子空间辨识方法(RORT)。首先,根据闭环系统状态空间模型和数据间投影关系,构建确定-随机模型,并利用GIVENS变换实现投影向量的递推QR分解;然后,引入带遗忘因子的辨识算法,构建广义能观测矩阵的递推更新形式,以减少子空间辨识算法中QR分解和SVD分解的计算量;最后,针对某型号陀螺仪闭环系统进行实验。实验结果表明, RORT法的辨识拟合度高于91%,能够对陀螺仪闭环系统模型参数进行在线监测。     

Recursive identification of time-varying systems: Self-tuning and matrix RLS algorithms

In this paper, a new parallel adaptive self-tuning recursive least squares (RLS) algorithm for time-varying system identification is first developed. Regularization of the estimation covariance matrix is included to mitigate the effect of non-persisting excitation. The desirable forgetting factor can be self-tuning estimated in both non-regularization and regularization cases. We then propose a new matrix forgetting factor RLS algorithm as an extension of the conventional RLS algorithm and derive the optimal matrix forgetting factor under some reasonable assumptions. Simulations are given which demonstrate that the performance of the proposed self-tuning and matrix RLS algorithms compare favorably with two improved RLS algorithms recently proposed in the literature.     

TH-UWB系统中基于自适应算法的接收机研究

提出了一种适用于跳时超宽带(TH-UWB)系统中的RAKE接收机方案。该方法利用基于梯度的可变遗忘因子的改进递推最小二乘(RLS)算法进行信道估计,并与基于经典RLS算法和基于最大似然概率(ML)算法的接收机方案进行对比。结果表明,这种新型RAKE接收机方案能够更有效地跟踪时变衰落信道的变化;在相同条件下,该方案能够提高系统性能,获得更小的误码率(BER)。     

Genetic least squares for system identification

The recursive least-squares algorithm with a forgetting factor has been extensively applied and studied for the on-line parameter estimation of linear dynamic systems. This paper explores the use of genetic algorithms to improve the performance of the recursive least-squares algorithm in the parameter estimation of time-varying systems. Simulation results show that the hybrid recursive algorithm (GARLS), combining recursive least-squares with genetic algorithms, can achieve better results than the standard recursive least-squares algorithm using only a forgetting factor.     

Performance analysis of multi-innovation stochastic Newton recursive algorithms

The stochastic Newton recursive algorithm is studied for system identification. The main advantage of this algorithm is that it has extensive form and may embrace more performance with flexible parameters. The primary problem is that the sample covariance matrix may be singular with numbers of model parameters and (or) no general input signal; such a situation hinders the identification process. Thus, the main contribution is adopting multi-innovation to correct the parameter estimation. This simple approach has been proven to solve the problem effectively and improve the identification accuracy. Combined with multi-innovation theory, two improved stochastic Newton recursive algorithms are then proposed for time-invariant and time-varying systems. The expressions of the parameter estimation error bounds have been derived via convergence analysis. The consistence and bounded convergence conclusions of the corresponding algorithms are drawn in detail, and the effect from innovation length and forgetting factor on the convergence property has been explained. The final illustrative examples demonstrate the effectiveness and the convergence properties of the recursive algorithms.     

A new information-weighted recursive algorithm for time-varying systems: application to UAV system identification

This paper presents a new recursive identification method which can efficiently estimate time-varying parameters in discrete time systems and has significant advantages over standard recursive least-squares (RLS) method. This new information-weighted recursive algorithm for time-varying systems has three novel features, discounting of inaccurate estimates through weighting by the Information matrix, using the reuse of past data in computing current parameter estimates, a new tuneable damping factor parameter and a precisely designed compensation term to neutralise the estimation error caused by time-varying coefficients. A rigorous proof of convergence is also provided. Simulations show that the new algorithm significantly outperforms standard RLS, exhibiting better tracking performance and faster convergence. Flight tests on a T-REX 800 helicopter Unmanned Aerial Vehicle platform show that it gives system parameter estimates that are accurate enough and converge quickly enough that flight controllers can be designed in real-time based on the online identified model.