Auxiliary Model‐Based Maximum Likelihood Multi‐Innovation Forgetting Gradient Identification for a Class of Multivariable Systems DOI Open Access
Huihui Wang, Ximei Liu

Optimal Control Applications and Methods, Год журнала: 2025, Номер unknown

Опубликована: Янв. 29, 2025

ABSTRACT Through dividing a multivariable system into several subsystems, this paper derives the sub‐identification model. Utilizing obtained model, an auxiliary model‐based maximum likelihood forgetting gradient algorithm is derived. Considering enhancing parameter estimation accuracy, multi‐innovation (AM‐ML‐MIFG) proposed taking advantage of identification theory. Simulation results test effectiveness algorithms, and confirm that AM‐ML‐MIFG has satisfactory performance in capturing dynamic properties system.

Язык: Английский

Hierarchical gradient‐ and least‐squares‐based iterative estimation algorithms for input‐nonlinear output‐error systems from measurement information by using the over‐parameterization DOI
Feng Ding, Ling Xu, Xiao Zhang

и другие.

International Journal of Robust and Nonlinear Control, Год журнала: 2023, Номер 34(2), С. 1120 - 1147

Опубликована: Окт. 17, 2023

Abstract This article investigates the parameter identification problems of stochastic systems described by input‐nonlinear output‐error (IN‐OE) model. IN‐OE model consists two submodels, one is an input nonlinear and other a linear The difficulty in that information vector contains unknown variables, which are noise‐free (true) outputs system, approach taken here to replace terms with auxiliary Based on over‐parameterization hierarchical principle, gradient‐based iterative algorithm least‐squares‐based proposed estimate parameters systems. Finally, numerical simulation examples given demonstrate effectiveness algorithms.

Язык: Английский

Процитировано

83

An identification algorithm of generalized time-varying systems based on the Taylor series expansion and applied to a pH process DOI
Yan Ji, Jian Liu, Haibo Liu

и другие.

Journal of Process Control, Год журнала: 2023, Номер 128, С. 103007 - 103007

Опубликована: Июнь 20, 2023

Язык: Английский

Процитировано

78

Decomposition and composition modeling algorithms for control systems with colored noises DOI
Ling Xu, Feng Ding

International Journal of Adaptive Control and Signal Processing, Год журнала: 2023, Номер 38(1), С. 255 - 278

Опубликована: Окт. 19, 2023

Summary This article proposes a novel identification framework for estimating the parameters of controlled autoregressive moving average (CARARMA) models with colored noise. By means building an auxiliary model and using hierarchical principle, this investigates highly‐efficient parameter estimation algorithm. In order to meet need identifying systems large‐scale parameters, whole CARARMA system is separated into two sets decomposition composition recursive algorithm (i.e., generalized extended least squares or decomposition‐based algorithm) presented. Moreover, analyzes convergence proposed The performance analysis shows that can reduce complexity compared without decomposition.

Язык: Английский

Процитировано

75

Recursive identification methods for general stochastic systems with colored noises by using the hierarchical identification principle and the filtering identification idea DOI
Feng Ding, Ling Xu, Xiao Zhang

и другие.

Annual Reviews in Control, Год журнала: 2024, Номер 57, С. 100942 - 100942

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

72

Filtered generalized iterative parameter identification for equation‐error autoregressive models based on the filtering identification idea DOI
Feng Ding, Xingling Shao, Ling Xu

и другие.

International Journal of Adaptive Control and Signal Processing, Год журнала: 2024, Номер 38(4), С. 1363 - 1385

Опубликована: Янв. 28, 2024

Summary By using the collected batch data and iterative search, based on filtering identification idea, this article investigates proposes a filtered multi‐innovation generalized projection‐based method, gradient‐based least squares‐based method for equation‐error autoregressive systems described by models. These methods can be extended to other linear nonlinear scalar multivariable stochastic with colored noises.

Язык: Английский

Процитировано

65

State of art on state estimation: Kalman filter driven by machine learning DOI
Yuting Bai, Bin Yan, Chenguang Zhou

и другие.

Annual Reviews in Control, Год журнала: 2023, Номер 56, С. 100909 - 100909

Опубликована: Янв. 1, 2023

Язык: Английский

Процитировано

61

Novel parameter estimation method for the systems with colored noises by using the filtering identification idea DOI
Ling Xu, Feng Ding, Xiao Zhang

и другие.

Systems & Control Letters, Год журнала: 2024, Номер 186, С. 105774 - 105774

Опубликована: Март 14, 2024

Язык: Английский

Процитировано

60

Unbiased recursive least squares identification methods for a class of nonlinear systems with irregularly missing data DOI
Wenxuan Liu, Meihang Li

International Journal of Adaptive Control and Signal Processing, Год журнала: 2023, Номер 37(8), С. 2247 - 2275

Опубликована: Июнь 6, 2023

Summary Missing data often occur in industrial processes. In order to solve this problem, an auxiliary model and a particle filter are adopted estimate the missing outputs, two unbiased parameter estimation methods developed for class of nonlinear systems (e.g., bilinear systems) with irregularly data. Firstly, is constructed unknown output, model‐based multi‐innovation recursive least squares algorithm presented by expanding scalar innovation vector. Secondly, according bias compensation principle, proposed compensate caused colored noise. Thirdly, further improving accuracy, true output estimated filter, filtering‐based developed. Finally, numerical example selected validate effectiveness algorithms. The simulation results indicate that algorithms have good performance identifying

Язык: Английский

Процитировано

59

Adaptive Multi-Innovation Gradient Identification Algorithms for a Controlled Autoregressive Autoregressive Moving Average Model DOI
Ling Xu, Huan Xu, Feng Ding

и другие.

Circuits Systems and Signal Processing, Год журнала: 2024, Номер 43(6), С. 3718 - 3747

Опубликована: Март 13, 2024

Язык: Английский

Процитировано

56

Multi‐innovation gradient‐based iterative identification methods for feedback nonlinear systems by using the decomposition technique DOI
Dan Yang, Feng Ding

International Journal of Robust and Nonlinear Control, Год журнала: 2023, Номер 33(13), С. 7755 - 7773

Опубликована: Июнь 3, 2023

Summary This paper studies the parameter estimation problems of feedback nonlinear systems. Combining multi‐innovation identification theory with negative gradient search, we derive a gradient‐based iterative algorithm. In order to reduce computational burden and further improve accuracy, decomposition algorithm is proposed by using technique. The key transform an original system into two subsystems estimate parameters each subsystem, respectively. A simulation example provided demonstrate effectiveness algorithms.

Язык: Английский

Процитировано

55