基于辅助模型的量化控制系统辨识方法

针对具有通信约束的量化控制系统模型, 在采用随机重复性试验测量信息的技术上, 提出了基于辅助模型的量化系统参数辨识方法. 首先分析了在随机重复性试验方法下量化系统的模型特征并给出了分两步辨识的策略.分析表明, 在上述模型里系统具有时变的估计误差, 推导了进行参数辨识所满足的持续激励条件, 并给出了基于辅助模型的多新息量化辨识递推算法. 接着研究了所给出辨识算法的收敛性分析, 得到了系统参数估计误差上界的计算式,最后将方法推广到一类Hammerstein非线性系统量化辨识问题上. 数字仿真验证了该算法及结论     

双率CARMA模型的广义最小方差自校正控制

针对一类双率采样的CARMA模型,研究了相关的自校正控制问题。基于双率采样以及含有噪声的数据,本文提出一个辅助模型来估计无法采样到的损失输出数据,并进一步采用随机梯度算法来估计模型参数。通过最小化最优预测输出的方差并结合Diophantine方程给出了基于辅助模型的广义最小方差自校正控制(AM-GMVSTC)策略。最后通过一个仿真例子说明提出算法的有效性。     

一般双率随机系统状态空间模型及其辨识

对于双率采样数据系统,本文使用提升技术,推导了双率系统的提升状态空间模型.对 于系统状态可测量的双率系统,利用最小二乘原理辨识提升系统模型的参数矩阵;对于状态不 可测的未知参数双率系统,利用递阶辨识原理,提出了双率系统递阶状态空间模型辨识方法,来 辨识系统的状态和参数.具体做法:基于获得的状态估计和提升系统的输入输出数据递归估计 系统参数,然后基于获得的参数估计,计算系统的状态.     

双率系统辅助模型框架下的随机牛顿递推辨识

针对双率系统, 采用基于辅助模型的改进随机牛顿递推算法辨识输出误差模型. 若当前参数估计对应的估计系统不稳定, 则出现中间不可测时刻输出估计发散, 辨识过程停止. 增加非线性模型与常规辅助模型一起为下步递推提供信息估计, 确保递推进行. 为避免出现输入不充分或者广泛时Hessian 阵奇异或者接近奇异的情况,在Hessian 阵的递推中增加对称正定矩阵. 最后给出了所提出辨识算法的一致收敛性证明.     

双率系统极点配置自校正控制算法

针对控制输入频率是输出采样频率整数倍的双率系统,研究了极点配置自校正控制方法.由于双率采样数据系统存在未知的采样间输出(即损失输出),所以传统输入输出等周期单率系统极点配置自校正控制方法不适用于双率系统.为了解决这一困难,本文直接利用双率输入输出数据估算系统模型参数和采样间输出,进一步把估计的模型参数代入极点配置方程,通过求解多项式Diophantine方程.推导了被控系统控制律,给出了双率极点配置自校正控制算法.一个仿真例子说明双率系统极点配置算法的控制效果.     

基于辅助模型正交匹配追踪的多输入系统迭代辨识算法

针对含有未知时滞的多输入输出误差系统的时滞与参数辨识问题,提出一种基于辅助模型的正交匹配追踪迭代算法.首先,由于各输入通道的时滞未知,通过设定输入回归长度,对系统模型进行过参数化,得到一个高维的辨识模型,且辨识模型中参数向量为稀疏向量;然后,基于辅助模型思想和正交匹配追踪算法,在每次迭代过程中,对参数向量和辅助模型的输出进行交互估计,即利用正交匹配追踪算法获得参数向量的估计,再利用参数估计值计算辅助模型的输出,并用辅助模型的输出值代替信息向量中的不可测信息项以更新参数估计;最后,根据参数向量的稀疏特征,获得系统的时滞估计.所提出算法可以利用少量的采样数据信息同时获得系统参数和时滞的估计值.仿真结果表明了所提出算法的有效性.     

非均匀采样数据系统的一种辨识方法

阐述了非均匀采样方案,推导了非均匀多率采样系统的状态空间模型,进一步获得了对应的传递函数模型.为解决辨识模型信息向量中存在未知变量的问题,使用辅助模型技术,用辅助模型的输出代替系统的未知变量,进而提出了非均匀采样数据系统的辅助模型随机梯度辨识算法.为了提高算法收敛速度和改善参数估计精度,在算法中引入遗忘因子,给出了相应的辅助模型带遗忘因子随机梯度算法.仿真结果表明,引入遗忘因子后,算法的收敛速度加快,参数估计精度提高.     

基于随机梯度的双率系统自校正控制方法

针对输入更新频率是输出刷新频率整数倍的未知参数双率系统,设计一个损失输出估计器计算采样间输出,再根据随机梯度算法设计参数估计器并得到系统模型的估计参数,基于最小方差控制原则设计出双率系统的自适应控制器。通过与基于最小二乘方法辨识系统参数的自适应控制算法进行比较,可以看出该算法的计算量较小,尤其是在输入数据更新频率与输出数据刷新频率相差较大时,计算量的差距更加明显。最后用仿真例子说明了该算法的有效性。     

具有预负载非线性特性的双率系统递推最小二乘估计算法

针对具有预负载非线性特性的双率系统, 提出一种新的辨识方法. 借助切换函数简化系统模型, 通过损失数据模型估计系统损失的输出数据, 进而利用系统所有输入和输出数据, 提出相应双率系统递推最小二乘算法. 与多项式转换方法相比, 该方法能够直接辨识出系统参数. 仿真结果验证了所提出方法的有效性.

     

双率采样系统的基于观测器的网络化H∞控制

研究一类带有网络传输时滞和丢包的双率采样系统的网络化H∞控制问题. 假设对象状态变量被分成两个分向量, 同一分向量的状态变量由同一类传感器以相同周期采样, 且两类传感器的采样频率不同. 采样后的分向量分别通过非理想网络传输到控制器端. 考虑到双率采样、网络传输时滞和丢包现象, 引入同步观测器来估计对象状态并设计基于估计状态的控制器来镇定双率采样系统. 基于这个思路, 将双率采样的网络化控制系统建模为带有两个时变时滞的连续系统. 利用Lyapunov-Krasovskii泛函方法, 以矩阵不等式形式给出该系统的稳定性判据和控制器设计方法. 最后, 通过数值例子验证所提方法的有效性.     

Combined parameter and output estimation of dual-rate systems using an auxiliary model

For a dual-rate sampled-data system, an auxiliary model based identification algorithm for combined parameter and output estimation is proposed. The basic idea is to use an auxiliary model to estimate the unknown noise-free output (true output) of the system, and directly to identify the parameters of the underlying fast single-rate model from the dual-rate input-output data. It is shown that the parameter estimation error consistently converges to zero under generalized or weak persistent excitation conditions and unbounded noise variance, and that the output estimates uniformly converge to the true outputs. An example is included.     

Parameter Identification and Intersample Output Estimation for Dual-Rate Systems

In this paper, we derive a mathematical model for dual-rate systems and present a stochastic gradient identification algorithm to estimate the model parameters and an output estimation algorithm to compute the intersample outputs based on the dual-rate input-output data directly. Moreover, we investigate convergence properties of the parameter and intersample estimation, and we test the proposed algorithms with example systems, including an experimental water-level system.     

State estimation and parameter identification method for dual-rate system based on improved Kalman prediction

For the dual-rate system, such as the process of space teleoperation whose control signals is partly determined by delayed feedback states, the state values and system parameters are coupled and influenced each other, which are hard to be estimated simultaneously. In this paper, we propose a novel method for this problem. Firstly, considering the asynchronism of the input and output sampling signals, an auxiliary model is modeled as a medium to the state and output functions. Secondly, the Kalman prediction algorithm is improved to estimate the state values at output signals of the dual-rate system. The general step is using the output estimated errors in original and auxiliary systems to modify the estimated state values of the auxiliary model, and then the unknown state values in original system is defined by the ones in auxiliary model. Based on improved Kalman algorithm and hierarchical identification algorithm, we present the detailed procedures of state estimation and parameter identification method for the dual-rate system. The processes of state estimation and parameter identification are calculated and modified alternately. Finally, the simulation results reveal that the state and parameters both approach to the real values and the state values converge faster than the parameters.     

Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model

Based on the work in Ding and Ding(2008),we develop a modifed stochastic gradient(SG)parameter estimation algorithm for a dual-rate Box-Jenkins model by using an auxiliary model.We simplify the complex dual-rate Box-Jenkins model to two fnite impulse response(FIR)models,present an auxiliary model to estimate the missing outputs and the unknown noise variables,and compute all the unknown parameters of the system with colored noises.Simulation results indicate that the proposed method is efective.     

A modified stochastic gradient based parameter estimation algorithm for dual-rate sampled-data systems

In this paper, we propose a novel identification algorithm for a class of dual-rate sampled-data systems whose input–output data are measured by two different sampling rates. A polynomial transformation technique is employed to derive a mathematical model for such dual-rate systems. The proposed modified stochastic gradient algorithm has faster convergence rate than stochastic gradient algorithms for parameter identification using the dual-rate input–output data. Convergence properties of the algorithm are analyzed. Finally, illustrative and comparison examples are provided to verify the effectiveness and performance improvement of the proposed method.     

Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model简

Based on the work in Ding and Ding （2008）, we develop a modifi ed stochastic gradient （SG） parameter estimation algorithm for a dual-rate Box-Jenkins model by using an auxiliary model. We simplify the complex dual-rate Box-Jenkins model to two fi nite impulse response （FIR） models, present an auxiliary model to estimate the missing outputs and the unknown noise variables, and compute all the unknown parameters of the system with colored noises. Simulation results indicate that the proposed method is effective.     

Performance Analysis of The Auxiliary‐Model‐Based Multi‐Innovation Stochastic Newton Recursive Algorithm for Dual‐Rate Systems

The stochastic Newton recursive algorithm is studied for dual‐rate system identification. Owing to a lack of intersample measurements, the single‐rate model cannot be identified directly. The auxiliary model technique is adopted to provide the intersample estimations to guarantee the recursion process continues. Intersample estimations have a great influence on the convergence of parameter estimations, and one‐step innovation may lead to a large fluctuation or even divergence during the recursion. In the meantime, the sample covariance matrix may appear singular. The recursive process would cease for these reasons. In order to guarantee the recursion process and to also improve estimation accuracy, multi‐innovation is utilized for correcting the parameter estimations. Combining the auxiliary model and multi‐innovation theory, the auxiliary‐model‐based multi‐innovation stochastic Newton recursive algorithm is proposed for time‐invariant dual‐rate systems. The consistency of this algorithm is analyzed in detail. The final simulations confirm the effectiveness of the proposed algorithm.     

Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model

This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.