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。     

闭环随机系统精选—优化辅助变量辨识算法的稳健性

在闭环自适应控制系统设计下对含有有色噪声和未建模动态的随机系统,采用精选一优化辅助变量辨识方法和极点配置控制,可使参数估计收敛;参数估计误差随未建模动态的衰减而减少。当未建模动态在平均意义下趋于零时,参数一致估计。     

应用最小二乘法辨识闭环系统

研究了有色噪声扰动下闭环系统参数的无偏估计问题.基于偏差补偿最小二乘辨识方法, 提出了一种用于闭环辨识的偏差补偿最小二乘法.这种方法不需要对噪声建模,即可获得闭 环系统前向通道和反馈通道传递函数中参数的渐近无偏估计.     

应用对消原理辨识传递函数

本文提出了一种辨识系统传递函数的新方法.它是通过引入预滤波器对输入信号进行 预处理,调整预滤波器的参数,使其实现与系统传递函数的部分对消,并应用对消原理而得到 的一种辨识方法.这种方法可以分别辨识传递函数的分子、分母及其因子,而且不需要对噪 声进行建模.此法还可以用来辨识误差模型.     

有色噪声中非线性动态系统的BP网络辨识

前馈多层神经网络为复杂的非线性系统提供了一种极具吸引力的模型结构。本文不利用仅含一个隐层的前馈多层神经网络来拟合离散时间非线性动态系统的问题进行了探讨。由于有色噪声的存在会导致网络模型偏差产生，文中引入了一种对噪声建模的方案。借助于非线性模型检验技术，本文给出了在有色噪声存在的情况下，利用ＢＰ网络辨识离散时间非线性动态系统的一般方法，仿真结果亦表明该方法行之有效。     

多自由度串联系统传递函数的辨识

本文考虑多自由度串联振动系统传递函数的辨识问题.分析表明,从输入输出结构看这 是一个多级闭环系统,如用模态分析技术建模,需解2n阶状态方程的特征值问题,随着n的增 大,计算量增大,参数估计精度下降.本文从系统辨识角度提出一种二阶振荡系统传递函数辨 识方法,分别处理各个自由度的传递函数辨识,仿真计算结果表明该方法比模态分析法简便有 效.     

递推鲁棒频域辨识算法

本文研究了带未建模动态系统的频域辨识问题，辨识结果为传递函在单位圆上的有限个点估计及其误差界。理论分析和仿真结果都表明，本文采取的求取误差界的方法比文［４］的方法导致更佳的估计和较小的误差界。     

基于整体辨识策略的非线性自适应控制方法

针对一类离散时间下的未知动态非线性系统,为解决传统自适应控制方法在交替辨识非线性系统时由于辨识精度低而导致的控制性能差的问题,本文提出了一种基于整体辨识策略的未建模动态补偿的自适应控制方法.利用随机向量函数链接(RVFL)网络的直链与增强结构特性挖掘其与低阶线性模型和高阶未建模动态项的等价对应关系,并融入权值偏差惩罚项,设计了网络模型参数在线更新算法辨识非线性系统参数.根据在线辨识的线性模型参数和未建模动态估计量,采用一步超前最优控制策略设计线性控制器和未建模动态补偿器.数值仿真表明,所提方法优于交替辨识下的非线性自适应控制方法,并通过工业应用的仿真研究验证所提方法在工业上的可用性.最后,对本文控制方法在实际应用中的潜在问题及理论受限条件的放松进行分析和展望.     

基于移动数据窗的传递函数多新息随机梯度辨识方法

一些工业过程可以近似用一个传递函数描述,结合统计辨识方法和非线性优化策略提出传递函数参数辨识方法.该方法采用动态数据方案,使用系统观测数据获得系统更多的模态信息.基于动态观测数据,提出传递函数随机梯度参数辨识方法.为进一步提高辨识精度,利用动态窗数据将随机梯度参数辨识方法中的标量新息扩展为新息向量,提出传递函数多新息随机梯度参数估计方法.最后通过仿真例子对所提出的方法进行了性能分析和模型验证.     

基于SVR的非线性动态系统建模方法研究

提出一种基于支持向量回归机(SVR)的非线性动态系统建模方法。用非线性静态子环节和线性动态子环节串联——Hammerstein模型来描述非线性动态系统。然后，通过函数展开将Hammerstein模型的非线性传递函数转换为等价的线性形式，从而建立起线性中间模型。再由SVR算法辨识出中间模型参数。最后，通过中间模型参数与Hammerstein模型参数之间的关系，实现原系统的非线性静态环节和线性动态环节的同时辨识。用非线性动态系统标定实验数据进行测试，建模结果表明所提方法具有如下优点：1)只需进行一次动态标定实验； 2)能给出非线性动态模型的数学解析表达式；3)充分利用SVR的优点，使所建模型具有更好的鲁棒性。该研究为非线性动态系统建模又提供了一种新方法。     

The least-squares identification of FIR systems subject to worst-case noise

The least-squares identification of FIR systems is analyzed assuming that the noise is a bounded signal and the input signal is a pseudo-random binary sequence. A lower bound on the worst-case transfer function error shows that the least-square estimate of the transfer function diverges as the order of the FIR system is increased. This implies that, in the presence of the worst-case noise, the trade-off between the estimation error due to the disturbance and the bias error (due to unmodeled dynamics) is significantly different from the corresponding trade-off in the random error case: with a worst-case formulation, the model complexity should not increase indefinitely as the size of the data set increases.     

具有输入及状态未建模动态系统的输出反馈自适应控制

针对一类具有输入及状态未建模动态的非线性系统, 设计K滤波器来估计系统不可量测状态, 基于动态面控制技术并利用径向基函数神经网络的逼近能力, 提出一种输出反馈自适应跟踪控制方案. 利用Nussbaum 函数性质, 有效地解决了高频增益符号未知问题. 在控制器设计中引入规范化信号来约束输入未建模动态, 从而有效地抑制其产生的扰动. 通过理论分析证明了闭环控制系统是半全局一致终结有界的.

     

FDI by extended Kalman filter parameter estimation for an industrial actuator benchmark

The extended Kalman filter (EKF) is formulated as a parameter estimator and used to estimate position sensor bias and actuator current bias signals for the industrial actuator benchmark system. These bias estimates are compared instantaneously to a threshold for fault detection and identification (FDI). The paper reports results for applying this method to given benchmark data. The FDI performance is good for detecting position sensor and actuator current faults in the presence of unmodeled nonlinear dynamics and an unmodeled load change for small-amplitude signal conditions when the EKF implementation assumes parameter pseudonoise and a slow decay in the parameter dynamics. For large-amplitude signals, the results are reasonably good, but they suggest that a more accurate model for a saturation nonlinearity could improve the method's FDI performance.     

Parametric and nonparametric identification of linear systems inthe presence of nonlinear distortions-a frequency domain approach

This paper studies the asymptotic behavior of nonparametric and parametric frequency domain identification methods to model linear dynamic systems in the presence of nonlinear distortions under some general conditions for random multisine excitations. In the first part, a related linear dynamic system (RLDS) approximation to the nonlinear system (NLS) is defined, and it is shown that the differences between the NLS and the RLDS can be modeled as stochastic variables with known properties. In the second part a parametric model for the RLDS is identified. Convergence in probability of this model to the RLDS is proven. A function of dependency is defined to detect and separate the presence of unmodeled dynamics and nonlinear distortions and to bound the bias error on the transfer function estimate     

Neural network-based robust adaptive control of nonlinear systems with unmodeled dynamics

A neural network-based robust adaptive control design scheme is developed for a class of nonlinear systems represented by input–output models with an unknown nonlinear function and unmodeled dynamics. By on-line approximating the unknown nonlinear functions and unmodeled dynamics by radial basis function (RBF) networks, the proposed approach does not require the unknown parameters to satisfy the linear dependence condition. It is proved that with the proposed control law, the closed-loop system is stable and the tracking error converges to zero in the presence of unmodeled dynamics and unknown nonlinearity. A simulation example is presented to demonstrate the method.     

Some comments on the use of normalization in robust adaptive control

This paper discusses the role of normalization with respect to robust parameter estimation for discrete-time adaptive control in the presence of unmodeled dynamics. It is pointed out that the normalizing signal may be brought into the adaptive law in two distinct ways; (i) via normalization of the regressor and the error signal in the standard parameter update law, and (ii) by replacement of the error signal with a function of the normalizing signal and the error. The convergence properties for both approaches are derived and shown to be similar.     

具有未建模动态的互联大系统事件触发自适应模糊控制

针对一类具有未建模动态及执行器故障的非严格反馈非线性互联大系统, 提出一种基于事件触发机制的模糊分散自适应输出反馈控制算法. 首先, 通过设计模糊状态观测器估计系统中不可测的状态, 并引入李雅普诺夫函数约束未建模动态. 然后, 提出一种基于事件触发机制的自适应容错控制器补偿多个执行器故障产生的影响. 最后, 利用障碍李雅普诺夫函数实现对系统输出的约束, 并证明闭环系统中所有信号均是半全局一致最终有界的, 且设计的事件触发机制可以避免Zeno行为. 数值仿真结果验证所提出设计方案的可行性及有效性.     

Quantifying the error in estimated transfer functions withapplication to model order selection

Previous results on estimating errors or error bounds on identified transfer functions have relied on prior assumptions about the noise and the unmodeled dynamics. This prior information took the form of parameterized bounding functions or parameterized probability density functions, in the time or frequency domain with known parameters. It is shown that the parameters that quantify this prior information can themselves be estimated from the data using a maximum likelihood technique. This significantly reduces the prior information required to estimate transfer function error bounds. The authors illustrate the usefulness of the method with a number of simulation examples. How the obtained error bounds can be used for intelligent model-order selection that takes into account both measurement noise and under-modeling is shown. Another simulation study compares the method to Akaike's well-known FPE and AIC criteria     

具有输入未建模动态的纯反馈非线性系统自适应控制

对一类具有状态和输入未建模动态且控制增益符号未知的纯反馈非线性系统,利用非线性变换、改进的动态面控制方法以及Nussbaum函数性质,提出两种自适应动态面控制方案.利用正则化信号来约束输入未建模动态,从而有效地抑制其产生的扰动.通过引入动态信号,有效地处理了由状态未建模动态引起的动态不确定性.通过在总的李雅普诺夫函数中引入非负正则化信号,并利用稳定性分析中引入的紧集,证明了闭环控制系统是半全局一致终结有界的.数值仿真验证了所提方案的有效性.     

碱赤泥浆流量的非线性智能PID控制

在生料浆配料过程中,碱赤泥浆的流量直接影响着生料浆的各项工艺指标.针对碱赤泥浆流量的非线性和不确定性,本文提出了基于非线性补偿的智能PID控制方法.基于非线性补偿的智能PID控制器由常规PID控制器和非线性动态补偿两部分组成,利用极点配置方法设计常规PID控制器,利用神经网络对碱赤泥浆流量对象中的非线性部分进行逼近并在控制器中进行补偿.设计并开发了碱赤泥浆流量的非线性智能PID控制软件,应用结果表明,碱赤泥浆的流量可以准确、稳定的跟踪设定值,具有良好的控制效果.