鲁棒辨识研究的现状

综述了鲁棒辨识问题的研究进展，介绍 了鲁棒辨识问题产生的背景，对各种不同的辨识方法进行了评述，并指出各自的特点，最后总结了今后的发展方向。     

最坏情况下l^1鲁棒辨识方法研究

系统地讨论了ＳＩＳＯ、线性时不变、指数稳定系统在最坏情况下的ｌ＾１鲁棒辨识问题。提出了系统模型集合的最小外框概念，建立了两种任意非零信号作用下ｌ＾１鲁棒辨识算法；提出了任意非零信号作用下系统的可辨识条件；证明了算法的全局收敛性和最优性。     

鲁棒辨识问题评述

本文综述了一类新的辨识问题－鲁棒辨识问题的研究进展，首先介绍了鲁棒辨识问题产生的背景，然后对各种不同的辨识方法进行了评述，并指出了各自的特点，最后总结了今后的研究方向。     

一种基于滤波参数在线辨识的鲁棒自适应滤波器

针对一类未建模动态和扰动下的非线性随机系统的状态估计问题,提出了一种基于滤波参数在线辨识的鲁棒自适应滤波器.该算法通过极小化状态估计误差的方差同时正交化相邻时刻的滤波残差,在线辨识状态预报误差和滤波残差的协方差,实现了对未建模动态和扰动的自适应动态补偿,因此对未建模扰动具有很强的鲁棒性.仿真中研究了一个非线性随机时滞系统,其参数存在缓变和突变,时滞会多次跳变,量测噪声发生了均值漂移和方差突变.算法对时滞和参数的联合估计效果令人满意.     

互质因子摄动模型集的闭环l1鲁棒辨识

鲁棒辨识问题已经逐渐成为国际控制界的一个研究热点，本文首先研究了互质因子摄动模型集的闭环ｌ＾１鲁棒辨识问题，建立了系统的Ａ图象拓扑，提出了ＢＩＢＯ不稳定系统的闭环ｌ＾１鲁棒辨识方法，由该方法得到的结果是具体代表性的互质因子摄动模型集。     

基于动态神经网的非线性鲁棒辨识

研究了一类基于动态神经网络的未知非线性多变量系统的鲁棒辨识问题，用Ｌｙａｐｕｎｏｖ稳定性理论获得了具有保护策略的鲁棒调权律，从理论上证明了被辨识的系统是鲁榛 ，辨识误差按建模误差和未建模动态收敛到一个稳定区域，该策略的特点是不需要离线学习又不需要对象的状态完全可测，仿真结果验证了提出的动态网鲁棒辨识策略的有效性。     

不确定性系统的鲁棒辨识及其发展

对适合控制的辨识即鲁棒辨识的发展作了综述。首先给出鲁棒辨识的缘由、提法和一些基本概念；然后讨论了集员辨识的理论和发展，Ｈ∞辨识以及部分结论；最后论述了适合控制的辨识理论的进一步发展。     

L1鲁棒辨识中的一般中心估计算法及其特例

本文分析具了Ｌ１误差的线性时不变系统的最不利情况的辨识问题，对系统假设的先验信息未知系统的脉冲响应函数控制稳定，并且假定实验数据具有噪声干扰的，本文提出了一般中心估计算法并分析了最不利情况的误差界限，并进一步研究了在某些特殊情况下，中心估计的简单求法及其性质，本文所给算法和辨识结果是面向鲁棒控制的。     

l1鲁棒辨识:一种递推插值方法

针对l1鲁棒辨识不能有效利用试验数据和进行在线辨识的问题,提出了一种在线递推插值辨识方法.用几何方法描述试验信息,利用系统可行集与新的试验信息所构成的半空间的包含关系判断数据信息,有效地利用了试验数据,提高了辨识精度.同时提出了一种新的计算辨识误差紧界的方法.仿真结果表明了算法的有效性和可行性.     

递推鲁棒频域辨识算法误差界计算的改进*

文［１］研究了带未建模动态系统的频域辨识问题，辨识的结果为传递函数在单位圆上的有限个点估计及其误差界。本文指出文［１］定理的错误，并对其进行了更正，数字仿真表明，更正的误差界比文［１］中的误差界更精确。     

Robust identification method for nonlinear model structures and its application to high-performance aircraft

A common assumption is that the model structure is known for modelling high performance aircraft. In practice, this is not the case. Actually, structure identification plays the most important role in the processing of nonlinear system modelling. The integration of mode structure identification and parameter estimation is an efficient method to construct the model for high performance aircraft, which is nonlinear and also contains uncertainties. This article presents an efficient method for identifying nonlinear model structure and estimating parameters for high-performance aircraft model, which contains uncertainties. The parameters associated with nonlinear terms are considered one after the other if they should be included in the nonlinear model until a stopping criterion is met, which is based on Akaike's information criterion. A numerically efficient U-D factorisation is presented to avoid complex computation of high-order matrices. The proposed method is applied to flight test data of a high-performance aircraft. The results demonstrate that the proposed method could obtain the good aircraft model with a reasonably good fidelity based on the comparison with flight test data.     

饱和系统鲁棒控制及其在飞行控制中的应用

给出一种饱和系统鲁棒稳定性的分析和设计方法.通过对不确定系统的H∞优化过程的分析,并根据其反馈控制原理,按全部和部分输入超过限制分别考虑不同指标下的优化计算问题,得到了输入受限制条件下的3种Riccati方程.飞行控制器设计和仿真结果表明,采用该方法可给出满意的控制效果.     

基于行列式比的阶次和参数同步递推辨识算法

针对输出误差模型,结合辅助模型的思想对原有阶次辨识和参数估计的方法进行融合和扩展,推导出基于辅助模型的行列式比定阶法,同时得出模型的阶次和参数,不仅减少了辨识过程的计算量,也节约了辨识时间。考虑到原有行列式比定阶法可能存在的不准确性,提出了一种系统模型的确认方法,增强了阶次辨识能力。仿真实验也充分表明,对行列式比定阶法的扩展不仅可以准确地辨识出系统的阶次,得出的参数估计值也有较高的精度。     

Robust identification/invalidation in an LPV framework

A robust linear parameter varying (LPV) identification/invalidation method is presented. Starting from a given initial model, the proposed method modifies it and produces an LPV model consistent with the assumed uncertainty/noise bounds and the experimental information. This procedure may complement existing nominal LPV identification algorithms, by adding the uncertainty and noise bounds which produces a set of models consistent with the experimental evidence. Unlike standard invalidation results, the proposed method allows the computation of the necessary changes to the initial model in order to place it within the consistency set. Similar to previous LPV identification procedures, the initial parameter dependency is fixed in advance, but here a methodology to modify this dependency is presented. In addition, all calculations are made on state‐space matrices which simplifies further controller design computations. The application of the proposed method to the identification of nonlinear systems is also discussed. Copyright © 2009 John Wiley & Sons, Ltd.     

On a method for model structure selection in system identification

Bohlin has recently suggested an appealing method for model structure selection. It is shown that the method can be interpreted as a modified F-test. The minimal loss in a larger model structure is substituted by an estimate. The estimate is obtained by minimization of a quadratic approximation of the loss function.     

推力矢量飞机鲁棒故障检测与辨识和指令滤波容错控制系统设计

针对推力矢量飞机(TVA)在超机动飞行中的舵面故障、执行器故障、参数摄动和外界干扰等问题,提出了一种鲁棒故障检测与辨识和指令滤波容错控制(RFDI-CFFTC)系统设计方法.首先针对TVA故障模型提出了一种基于多观测器的RFDI机制,通过引入自适应律和观测器来补偿参数摄动和外界干扰的影响,实现对舵面故障和执行器故障的准...     

交通流模型参数的近似动态规划辨识方法

针对交通流模型的强非线性、不确定性等特点,提出了基于近似动态规划的交通流模型参数辨识算法.该算法具有自学习和自适应的特性,不依赖于被控对象的解析模型.严格的理论推导证明了这种参数辨识方案的收敛性,仿真结果验证了所提出算法的有效性.     

UD分解与偏差补偿结合用于变量带误差模型辨识

本文提出一种基于UD(upper-diagonal)分解与偏差补偿结合的辨识方法,用于变量带误差(errors-in-variables,EIV)模型辨识.考虑单输入单输出(single input and single output,SISO)线性动态系统,当输入和输出含有零均值、方差未知的高斯测量白噪声时,该类系统的模型参数估计是一种典型的EIV模型辨识问题.为了获得这种EIV模型参数的无偏估计,本文先推导出最小二乘模型参数估计偏差量与输入输出噪声方差以及最小二乘损失函数与输入输出噪声方差的关系,然后采用UD分解方法递推获得模型参数估计值,再利用输入输出噪声方差估计值补偿模型参数估计偏差,以此获得模型参数的无偏估计.本文还讨论了算法实现过程中遇到的一些问题及修补方法,并通过仿真例验证了所提辨识方法的有效性.     

Adaptive trajectory generation based on real-time estimated parameters for impaired aircraft landing

ABSTRACT

This paper is motivated by a need to address the challenge of securing a safe landing after suffering from inflight impairment. In this paper, a new adaptive generalised model predictive static programming (G-MPSP) is developed to generate a safe emergency landing trajectory for impaired aircraft. Utilising the computationally efficient G-MPSP framework, the proposed algorithm enables adaptation of model parameters based on the prediction errors to ensure reasonable guidance performance. Based on the estimated parameters, a feasible landing trajectory is then generated by the flexible finite-horizon G-MPSP with input constraints. The integrated approach features explicit closed-form solutions for both parameter estimation and trajectory generation. Its effectiveness is demonstrated by simulations in the presence of parameter uncertainties and noises and by comparison studies with the non-adaptive G-MPSP.     

An instrumental variable method for model order identification

The paper describes a simple instrumental variable method for identifying the structure of a wide class of time-series models. The method is aimed at providing a parametrically efficient (parsimonious) model structure which will lead to a combination of low residual error variance, i.e. a good explanation of the data, and low parametric estimation error variance (as measured by some norm associated with the covariance matrix of the estimation errors). It can be applied to single input-single output and multivariable systems using either discrete or continuous-time series models. It can also function as a recursive (on-line) test for reduction in model order.