基于Laguerre模型的过热汽温自适应预测PI控制系统

针对火电厂锅炉过热汽温控制的特点,设计1种基于Laguerre模型的自适应预测PI控制器。该预测控制器采用对时延具有良好逼近能力的正交Laguerre函数模型作为预测模型,利用带遗忘因子的最小二乘法在线辨识Laguerre预测模型的系数,以提高系统适应工况变化的能力;滚动优化指标采用比例积分型结构,以提高系统的快速性和鲁棒性。通过对具有严重参数不确定性、扰动以及大迟滞的电厂过热汽温被控对象进行仿真研究,结果表明该方法能够很好地适应对象特性的变化,且控制系统的性能比常规串级控制系统有较大提高。     

Recursive least squares estimation methods for a class of nonlinear systems based on non-uniform sampling

Many dynamic processes in practice have nonlinear characteristics and must be described by using nonlinear models. It remains to be a challenging problem to build the models of such nonlinear systems and to estimate their parameters. This article studies the parameter estimation problem for a class of Hammerstein-Wiener nonlinear systems based on non-uniform sampling. By means of the auxiliary model identification idea, an auxiliary model-based recursive least squares algorithm is derived for the systems. In order to enhance the computational efficiency, an auxiliary model-based hierarchical least squares algorithm is proposed by utilizing the hierarchical identification principle. The simulation results confirm the effectiveness of the proposed algorithms.     

A fuzzy C‐regression model algorithm using a new PSO algorithm

In this paper, a new methodology is introduced for the identification of the parameters of the multiple‐input–multiple‐output local linear Takagi‐Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi‐Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature.     

基于改进最小二乘法永磁同步电机多参量辨识

针对永磁同步电机(PMSM)多参量辨识数学模型欠秩的情况,将电机参数分为快变化和慢变化2种类型参数,结合慢参量入口和电机稳态信息建立慢辨识模型和快辨识模型,提出了一种改进的分步迭代在线辨识方法。该方法基于电机在旋转坐标系下的稳态电压方程,分时分步固定慢变化参数,利用遗忘因子递推最小二乘算法,能够辨识表贴式永磁同步电机(SPMSM)的3个电气参数,计算压力小,辨识速度快,适于工程应用。以DSP控制器和测功机试验平台为基础的试验证明了辨识方法的可行性。     

一种全局收敛的时滞连续系统零极点辨识算法

针对一类时滞连续系统的模型参考自适应递推辨识算法, 运用微分中值定理,在参数不确定性的情况下进行了辨识算法的收敛性分析。通过对包括增益、时滞和实零极点在内的参数不确定性所进行的收敛性讨论, 提出了一种对模型误差进行低通滤波的改进算法, 以改善辨识算法的收敛效果     

Adaptive estimation for periodic signal enhancement and tracking

A fast algorithm for enhancement and tracking of periodic signals with fixed or slowly varying central frequency in additive noise is proposed. the algorithm is based on results from discounted least squares identification of a harmonic signal model combined with a recursive update scheme for the estimated fundamental frequency. In particular the gain sequence of the algorithm is determined by the fundamental frequency and an eligible forgetting factor. the performance of the algorithm is compared with theoretical lower bounds and it is shown that the algorithm yields good results.     

Delta算子离散模型辨识的性能分析

研究了Delta算子描述的离散系统参数辨识问题，基于Delta算子矩阵求逆引理，给出Delta算子递推最小二乘(DRLS)估计公式；分析了DRLS算法的参数误差和预报误差特性。所得结论将连续与离散模型辨识的有关结果统一于Delta算子框架。     

Optimal expansions of multivariable ARX processes on Laguerre bases via the Newton–Raphson method

In this article, an optimal linear MIMO system approximation by using discrete‐time MIMO autoregressive with exogenous input (ARX) model is proposed. Each polynomial function of the MIMO ARX model associated with the inputs and with the outputs is expanded on independent Laguerre orthonormal basis. The resulting model is entitled MIMO ARX–Laguerre model. The optimal approximation of which is ensured once the poles characterizing each Laguerre orthonormal basis are set to their optimal values. In this paper, a new method to estimate, from input/output measurements, the optimal Laguerre poles of the MIMO ARX–Laguerre model is proposed. The method consists in applying the Newton–Raphson's iterative technique in which the gradient and the Hessian are expressed analytically. The proposed algorithm is tested on a numerical example and on a benchmark system. Simulation results show the effectiveness of the proposed optimal modeling method. Copyright © 2015 John Wiley & Sons, Ltd.     

Least‐squares parameter estimation for systems with irregularly missing data

This paper considers the problems of parameter identification and output estimation with possibly irregularly missing output data, using output error models. By means of an auxiliary model (or reference model) approach, we present a recursive least‐squares algorithm to estimate the parameters of missing data systems, and establish convergence properties for the parameter and missing output estimation in the stochastic framework. The basic idea is to replace the unmeasurable inner variables with the output of an auxiliary model. Finally, we test the effectiveness of the algorithm with an example system. Copyright © 2009 John Wiley & Sons, Ltd.     

Development of a new self-tuning control algorithm for finite and infinite horizon quadratic adaptive optimization

The theory of implicit models, introduced in previous papers, is used here in order to define a new adaptive control algorithm based on either m-step-ahead or infinite horizon LQ optimization and on recursive least squares identification techniques in the presence of systems having an ARMAX structure. The adaptive algorithm is based on the identification of a single ARX implicit model, which is defined as a model capable of representing the system input-output behaviour correctly only in certain closed-loop conditions. It is shown that, by properly structuring the algorithm, a single whitening (i.e. yielding a white residual sequence) possible convergence point exists coinciding with the optimal control law. Simple conditions assuring that a generic convergence point coincides with the above one are also provided, as well as preliminary simulation experience.     

Convergence analysis of the RLS identification algorithm with exponential forgetting in stationary ARX‐structures

The recursive least‐squares (RLS) identification algorithm is often extended with exponential forgetting as a tool for parameter estimation in time‐varying stochastic systems. The statistical properties of the parameter estimates obtained from such an extended RLS‐algorithm depend in a non‐linear way on the time‐varying characteristics and on the forgetting factor. In this paper, the RLS‐estimator with exponential forgetting is applied to time‐invariant Gaussian autoregressions with second‐order stationary external inputs, i.e.to Gaussian ARX‐processes. Approximate expressions for the asymptotic bias and covariance of the parameter estimates when the forgetting factor tends to one and time to infinity are given, showing that the bias is non‐zero and that the covariance function decays exponentially with a rate that is given by the forgetting factor. The orders of magnitude of the errors in the asymptotic expressions are also derived. Copyright © 1999 John Wiley & Sons, Ltd.     

基于拉盖尔模拟神经网络的过热汽温直接自适应控制系统

提出一种Laguerre(拉盖尔)模拟复合正交神经网络并应用于电厂过热汽温的直接自适应控制。模拟神经网络被作为直接自适应控制器,这种单隐层正交神经网络是基于Laguerre复合正交多项式函数,并具有在线连续学习的简单算法,且学习算法与被控对象模型无关。由于采用3层网络结构,输入层与隐层之间不用权值调整,在学习算法中只要在输出层与隐层之间寻找最佳权值,因此网络学习速度较快。网络隐层节点(处理元)是Laguerre多项式展开项,展开项的多少决定着网络的学习速度和精度。通过对具有严重参数不确定性、扰动以及大迟延的电厂过热汽温被控对象进行仿真研究,结果表明控制系统性能优于常规的PI控制系统。     

动态工况电池在线参数辨识及SOC估计研究

基于电池模型的荷电状态(SOC)估计方法,其估计精度主要取决于模型的精度。电池在动态工况下,输入电流变化激烈,传统的辨识方法因其收敛性差,导致模型精度降低。为了提高动态工况下电池模型精度,对传统带遗忘因子最小二乘法(FFRLS)进行改进,通过设置精度阈值,引入梯度矫正的方法,提出了改进带遗忘因子递推最小二乘法(IFFRLS)。利用改进算法进行在线参数辨识,建立二阶RC等效电路模型,与其他传统参数辨识建立的模型进行对比,验证IFFRLS对模型精度提高的有效性,模型平均误差为0.003 8 V。最后,将不同辨识方法所建立的模型与扩展卡尔曼滤波(EKF)算法进行联合估计SOC并对比其误差,结果表明通过IFFRLS辨识出来的高精度模型可有效提高SOC的估计精度,DST工况下,误差在1.51%以内。     

基于VFFRLS算法的锂电池参数辨识

动力电池性能是影响电动汽车综合性能的关键因素,因此准确辨识锂离子电池模型的参数对后续电池系统的荷电状态估计和健康状态预测至关重要。为了提高锂离子电池模型参数辨识算法的精度,以磷酸铁锂电池作为研究对象,建立电池二阶RC等效电路模型,并采用基于变量遗忘因子的最小二乘算法对锂离子电池模型进行在线参数辨识。通过搭建测试平台进行充放电实验,基于2种不同工况的实验数据,分别用文中算法、递推最小二乘算法和传统的带遗忘因子的最小二乘算法进行参数辨识,根据辨识结果估计出的端口电压与实验测试得到的实际值的误差比较来描述文中算法辨识结果的准确度。实验结果表明,基于变量遗忘因子的最小二乘算法在锂电池参数辨识方面表现出快速的收敛性和较高的估计精度。     

最小支持向量机在系统逆动力学辨识与控制中的应用

为克服支持向量机(support vector machine,SVM)在线辨识过程需要较大的内存开销的问题,该文将递推最小二乘法(recursive least square,RLS)与最小二乘支持向量机(least squares support vector machine,LS-SVM)回归相结合,利用RLS在线调整支持向量机的权向量和偏移量,实现了系统逆动力学模型的在线辨识。在获得逆动力学模型的基础上,设计了一种基于逆动力学递推最小二乘支持向量机的控制算法,利用RLS在线调整控制器参数。过热汽温辨识和控制的仿真结果表明,辨识出的逆动力学模型具有较高的精度,所设计的控制器能获得较好的控制性能和有较强的适应能力。     

Adaptive predictive control algorithm based on Laguerre Functional Model

Laguerre Functional Model has many advantages such as good approximation capability for the variances of system time‐delay, order and other structural parameters, low computational complexity, and the facility of online parameter identification, etc., so this model is suitable for complex industrial process control. A series of successful applications have been gained in linear and non‐linear predictive control fields by the control algorithm based on Laguerre Functional Model, however, former researchers have not systemically brought forward the theoretical analyses of the stability, robustness, and steady‐state performance of this algorithm, which are the keys to guarantee the feasibility of the control algorithm fundamentally. Aimed at this problem, we introduce the principles of the Incremental Mode Linear Laguerre Predictive Control (IMLLPC) algorithm, and then systemically propose the theoretical analyses and proofs of the stability and robustness of the algorithm, in addition, we also put forward the steady‐state performance analysis. At last, the control performances of this algorithm on two different physical industrial plants are presented in detail, and a number of experimental results validate the feasibility and superiority of IMLLPC algorithm. Copyright © 2005 John Wiley & Sons, Ltd.     

Maximum likelihood hierarchical least squares-based iterative identification for dual-rate stochastic systems

For a dual-rate sampled-data stochastic system with additive colored noise, a dual-rate identification model is obtained by using the polynomial transformation technique, which is suitable for the available dual-rate measurement data. Based on the obtained model, a maximum likelihood least squares-based iterative (ML-LSI) algorithm is presented for identifying the parameters of the dual-rate sampled-data stochastic system. In order to improve the computation efficiency of the algorithm, the identification model of a dual-rate sampled-data stochastic system is divided into two subidentification models with smaller dimensions and fewer parameters, and a maximum likelihood hierarchical least squares-based iterative (H-ML-LSI) algorithm is proposed for these subidentification models by using the hierarchical identification principle. The simulation results indicate that the proposed algorithms are effective for identifying dual-rate sampled-data stochastic systems and the H-ML-LSI algorithm has a higher computation efficiency than the ML-LSI algorithm.     

Load identification and voltage stability monitoring

The paper describes an algorithm for recursive identification of the power system composite load by using recursive least squares. The variation of the composite load components during slow voltage transients has been shown to deteriorate voltage stability margins. By combining the load identification and voltage stability monitoring, it is possible to make a better assessment of the risks associated with power system operation under heavy loading conditions     

Combined state and parameter estimation for Hammerstein systems with time delay using the Kalman filtering

This paper discusses the state and parameter estimation problem for a class of Hammerstein state space systems with time delay. Both the process and the measurement noises are considered in the system. On the basis of the observable canonical state space form and the key term separation, a pseudolinear regressive identification model is obtained. For the unknown states in the information vector, the Kalman filter is used to search for the optimal state estimates. A Kalman filter–based least squares iterative and a recursive least squares algorithms are proposed. Extending the information vector to include the latest information terms, which are missed for the time delay, the Kalman filter–based recursive extended least squares algorithm is derived to obtain the estimates of the unknown time delay, parameters, and states. The numerical simulation results are given to illustrate the effectiveness of the proposed algorithms.