共查询到20条相似文献，搜索用时 15 毫秒
1.
Robert J. Elliott Jason J. Ford John B. Moore 《International Journal of Adaptive Control and Signal Processing》2002,16(6):435-453
In this paper we discuss parameter estimators for fully and partially observed discrete‐time linear stochastic systems (in state‐space form) with known noise characteristics. We propose finite‐dimensional parameter estimators that are based on estimates of summed functions of the state, rather than of the states themselves. We limit our investigation to estimation of the state transition matrix and the observation matrix. We establish almost‐sure convergence results for our proposed parameter estimators using standard martingale convergence results, the Kronecker lemma and an ordinary differential equation approach. We also provide simulation studies which illustrate the performance of these estimators. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
2.
Yawen Mao Feng Ding Erfu Yang 《International Journal of Adaptive Control and Signal Processing》2017,31(10):1388-1400
In this paper, by means of the adaptive filtering technique and the multi‐innovation identification theory, an adaptive filtering‐based multi‐innovation stochastic gradient identification algorithm is derived for Hammerstein nonlinear systems with colored noise. The new adaptive filtering configuration consists of a noise whitening filter and a parameter estimator. The simulation results show that the proposed algorithm has higher parameter estimation accuracies and faster convergence rates than the multi‐innovation stochastic gradient algorithm for the same innovation length. As the innovation length increases, the filtering‐based multi‐innovation stochastic gradient algorithm gives smaller parameter estimation errors than the recursive least squares algorithm. 相似文献
3.
Yu Jin Feng Ding 《International Journal of Adaptive Control and Signal Processing》2024,38(2):513-533
In order to solve the problem of the parameter identification for large-scale multivariable systems, which leads to a large amount of computation for identification algorithms, two recursive least squares algorithms are derived according to the characteristics of the multivariable systems. To further reduce the amount of computation and cut down the redundant estimation, we propose a coupled recursive least squares algorithm based on the coupling identification concept. By coupling the same parameter estimates between sub-identification algorithms, the redundant estimation of the subsystem parameter vectors are avoided. Compared with the recursive least squares algorithms, the proposed algorithm in this article have higher computational efficiency and smaller estimation errors. Finally, the simulation example tests the effectiveness of the algorithm. 相似文献
4.
Zhen Kang Yan Ji Ximei Liu 《International Journal of Adaptive Control and Signal Processing》2021,35(11):2276-2295
This article considers the parameter estimation problem of Hammerstein nonlinear autoregressive output-error systems with autoregressive moving average noises. Applying the key term separation technique, the original system is decomposed into three subsystems: the first subsystem contains the unknown parameters related to the output, the second subsystem contains the unknown parameters related to the input, and the third subsystem contains the unknown parameters related to the noise model. A hierarchical recursive least squares algorithm is proposed based on the hierarchical identification principle for interactively identifying each subsystem. The simulation results confirm that the proposed algorithm is effective in estimating the parameters of Hammerstein nonlinear autoregressive output-error systems. 相似文献
5.
Ting Cui Feiyan Chen Feng Ding Jie Sheng 《International Journal of Adaptive Control and Signal Processing》2020,34(5):590-613
This article addresses the combined estimation issues of parameters and states for multivariable systems in the state-space form disturbed by colored noises. By utilizing the Kalman filtering principle and the coupling identification concept, we derive a Kalman filtering based partially coupled recursive generalized extended least squares (KF-PC-RGELS) algorithm to jointly estimate the parameters and the states. Using the past and the current data in parameter estimation, we propose a Kalman filtering based multi-innovation partially coupled recursive generalized extended least-squares algorithm to enhance the parameter estimation accuracy of the KF-PC-RGELS algorithm. Finally, a simulation example is provided to test and compare the performance of the proposed algorithms. 相似文献
6.
Ting Cui Ling Xu Feng Ding Ahmed Alsaedi Tasawar Hayat 《International Journal of Adaptive Control and Signal Processing》2020,34(11):1658-1676
Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms. 相似文献
7.
对含有不可观测量的同步发电机模型基本参数进行辨识,需要求解复杂的微分方程组,增加了辨识难度。提出一种由可观测量表示的同步发电机阻抗矩阵传递函数模型,简化了参数辨识方法,减小了辨识的计算量。利用阻抗实部和虚部分开表征的辨识算法进行模型的可辨识性分析,研究表明,结合稳态方程后,所提模型的基本参数是唯一可辨识的,避免了参数多值性问题,且辨识过程与参数经验值无关,能有效防止出现由参数经验值误差引起辨识精度降低的问题。通过自适应滤波获得信号的频域信息,结合粒子群优化算法辨识得到同步发电机基本参数。算例仿真结果验证了所提模型的正确性和辨识算法的有效性。 相似文献
8.
Yingjie Hu Linfeng Gou Ding Fan Herbert Ho-Ching Iu Tyrone Fernando Xinan Zhang 《International Journal of Adaptive Control and Signal Processing》2021,35(12):2389-2405
In this study, an adaptive model predictive control (MPC) strategy is proposed for a class of discrete-time linear systems with parametric uncertainty. In the presented adaptive MPC, an updating law is firstly designed to update the estimated parameters online. By utilizing the estimated parameters, a standard MPC optimization problem for unconstrained systems is established. Then, to deal with constrained systems, the min–max MPC technique is developed under the set-based approach for the estimated parameters. Furthermore, it is shown theoretically that the recursive feasibility and closed-loop stability can be rigorously proved, respectively. Finally, numerical simulations and comparative analysis are presented to illustrate the superiority of the proposed algorithms in control performance. 相似文献
9.
电力系统阻尼控制中的在线递推闭环子空间辨识 总被引：1，自引：0，他引：1
提出了电力系统阻尼控制中状态空间模型的在线递推闭环子空间辨识算法。在闭环条件下,基于广域测量信息,在线地辨识了包含主导低频振荡模式的系统降阶状态空间模型,并依此在线设计和更新线性二次最优部分输出辅助区间阻尼控制器以抑制区间低频振荡模式。算法具有良好的数值稳定性和较低的时间复杂度,能够实现系统模型的递推更新和辅助阻尼控制器参数的在线调整。8机36节点系统的仿真验证了算法的有效性。 相似文献
10.
Jing Na Juan Yang Xuemei Ren Yu Guo 《International Journal of Adaptive Control and Signal Processing》2015,29(8):1055-1072
The vast majority of available parameter estimation methods assume that the parameters to be estimated are constant or slowly time‐varying and mainly depend on a predictor or observer design so that a large adaptive gain must be used to achieve fast adaptation; this may result in high‐frequency oscillations when the system subjects to a large source of uncertainties or disturbances. This paper is concerned with adaptive online estimation of time‐varying parameters for two kinds of linearly parameterized nonlinear systems. By dividing the time into small intervals, the time‐varying parameters are approximated in terms of polynomials with unknown coefficients. Then a novel adaptive law design methodology is developed to estimate those constant coefficients, for which the parameter estimation error information is explicitly derived and used to drive the adaptations. To guarantee the continuity of the parameter estimation for all time, a parameter resetting scheme is introduced at the beginning of each interval. Finite‐time estimation convergence and the robustness against disturbances are all proved. Extensive simulation examples are provided to demonstrate the efficacy of the proposed algorithms for estimating time‐varying parameters. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
11.
本文运用现代控制理论的系统辨识方法,对所建立的异步电机数学模型中的不易测出的漏抗参数值进行拟合求解,提供了确定异步电机漏抗参数的又一种方法。从理论上和方法上分析,这样算得的结果要精确于用常规实验所测取的值。最后,利用所得到的参数进行瞬态仿真运算求解,得到的结果与瞬态实测值相符合。 相似文献
12.
13.
传统电机参数辨识采用固定脉冲宽度调制(PWM)波占空比。该方法导致电流大小不可控,而辨识电流大小又直接影响辨识准确性。为此,提出新的参数离线辨识方法,采用恒电流辨识原理并检测电机相电流,控制辨识电流在给定值附近波动,解决了电机辨识电流不合适问题。设计了续流二极管和IGBT电压模型,d轴和q轴电感辨识都采用在270°而非0°进行辨识,以此提高了辨识精度。最后,在变频冰箱上进行验证并与原来数据进行对比。该方法很好地解决了冰箱电机带背压条件下,电机d、q轴电感辨识不准确、辨识时间长等问题,试验结果证明了该方法的可行性和有效性。 相似文献
14.
Feng Ding Xingling Shao Ling Xu Xiao Zhang Huan Xu Yihong Zhou 《International Journal of Adaptive Control and Signal Processing》2024,38(4):1363-1385
By using the collected batch data and the iterative search, and based on the filtering identification idea, this article investigates and proposes a filtered multi-innovation generalized projection-based iterative identification method, a filtered generalized gradient-based iterative identification method, a filtered generalized least squares-based iterative identification method, a filtered multi-innovation generalized gradient-based iterative identification method and a filtered multi-innovation generalized least squares-based iterative identification method for equation-error autoregressive systems described by the equation-error autoregressive models. These filtered generalized iterative identification methods can be extended to other linear and nonlinear scalar and multivariable stochastic systems with colored noises. 相似文献
15.
16.
Ling Xu Feiyan Chen Feng Ding Ahmed Alsaedi Tasawar Hayat 《International Journal of Adaptive Control and Signal Processing》2021,35(5):676-693
This paper studies the problem of parameter estimation for the multifrequency sine signals, which have multiple characteristic parameters such as the amplitudes, phases, and frequencies. It is noted that the signal output is nonlinear with respect to the phase and frequency parameters while it is linear with respect to the amplitude parameters. This feature inspires us to separate all of the characteristic parameters into a linear parameter set and a nonlinear parameter set, where the linear set is composed of the amplitude parameters and the nonlinear set is composed of the phase parameters and the frequency parameters. After the parameter separation, two identification submodels are constructed for optimizing the linear parameter set and the nonlinear parameter set. Then the nonlinear identification model becomes a linear identification submodel and a nonlinear identification submodel. Therefore, the nonlinear optimization for minimizing the objective function is converted into the combination of the quadratic optimization and nonlinear optimization. Based on the separable identification submodels, a recursive least squares subalgorithm and a recursive gradient subalgorithm are proposed for identifying the linear parameters and nonlinear parameters, respectively. Moreover, an interactive estimation algorithm is designed to remove the related parameter sets between the subalgorithms and a hierarchical identification method is presented by combining the subalgorithms. For the purpose of tracking the time-varying, a forgetting factor is introduced to improve the convergence speed. The numerical examples are provided to qualify the performance of the proposed method based on some performance measures. 相似文献
17.
Ling Xu Feng Ding Lijuan Wan Jie Sheng 《International Journal of Adaptive Control and Signal Processing》2020,34(7):937-954
This article is concerned with the parameter identification problem of nonlinear dynamic responses for the linear time-invariant system by means of an impulse excitation signal and discrete observation data. Using the impulse signal as the input, the impulse response experiment is carried out and the dynamical moving sampling is designed to generate the measured data for deriving new identification algorithms. By applying the moving window data that contain the dynamical information of the system to be identified, an objective function with respect to the parameters of the systems is constructed according to the impulse response. In accordance with different functional relations between the system parameters and the system output response, the unknown parameter vector of the system is separated into a linear parameter vector and a nonlinear parameter vector. Based on the separated parameter vectors, two subidentification models are constructed and a separable identification algorithm is presented through the gradient search to improve the accuracy. Moreover, for the purpose of enhancing the estimation accuracy and capturing the dynamical feature of the systems, the moving window data are employed to develop the separable identification algorithm. The performance of the proposed separable identification method is illustrated via a numerical example. 相似文献
18.
C. Vural W. A. Sethares 《International Journal of Adaptive Control and Signal Processing》2005,19(8):601-622
This paper presents an adaptive autoregressive (AR) approach to the blind image deconvolution problem which has several advantages over standard adaptive FIR filters. There is no need to figure out the optimum filter support when using an AR deconvolution filter because it is the same as the support of the blur. Thus there is no distortion introduced by the finite support of the FIR filter. While an FIR filter provides an approximate inverse to the blur at convergence, the AR filter converges to an approximation of the blur itself. Hence, the method can be used for blur identification. Simulations suggest that convergence of the adaptive AR filter coefficients occur rapidly and the improvement in signal‐to‐noise ratios are higher than in the FIR case for a given blur (and with the same step‐size for the adaptive algorithms). When the adaptive AR method is derived naively to minimize the dispersion, it requires a recursion within a recursion which is computationally complex. We propose a simplification that removes the inner recursion, and prove conditions under which this simplification is valid when dealing with binary images. Simulations are used to show that the method may also be applied to certain multi‐valued images as well. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
19.
Gregory L. Plett 《International Journal of Adaptive Control and Signal Processing》2002,16(4):243-272
Adaptive‐signal‐processing techniques have been employed with great success in such applications as: system identification, channel equalization, statistical prediction and noise/echo cancellation. From a mathematical point of view, there is little difference between these applications and the types of operations required by control systems to control a dynamical system. This paper presents an approach to control systems called adaptive inverse control in which adaptive‐signal‐processing techniques are used throughout. Adaptive inverse control comprises three simultaneous processes. The plant is automatically modeled using adaptive system identification techniques. The dynamic response of the system is adaptively controlled using the resulting model and methods related to channel equalization. Adaptive disturbance canceling is performed using methods similar to noise canceling. The method applies directly to stable single‐input single‐output (SISO) and multi‐input multi‐output (MIMO) plants, and does not require an a priori model of the system. If the plant is unstable, it must first be stabilized using conventional feedback. This implies that at least a rudimentary model need be made if the plant is unstable. Once the plant is stabilized, adaptive inverse control may be applied to the stabilized system. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
20.
多个不良数据（遥测坏数据和参数错误）存在时,现有参数辨识与估计方法很难保证其结果的有效性。为此．文中提出了考虑分区的电网参数辨识与估计方法。首先,基于最小度拓扑搜索,将原网络分解为辐射子网、简单环网和复杂环网,形成多个独立子区域;其次,对每个独立子分区,采用拉格朗日辨识法辨识不良数据;最后．对每个可疑参数进行考虑零注入功率约束的加权最小二乘增广估计。该方法避免了不同子区域之间不良数据的相互影响,提高了多个不良数据辨识的有效性与错误参数估计的精度。基于IEEE30节点系统的算例计算,验证了该方法的有效性。 相似文献