Recursive identification methods for general stochastic systems with colored noises by using the hierarchical identification principle and the filtering identification idea

Annual Reviews in Control, Journal Year: 2024, Volume and Issue: 57, P. 100942 - 100942

Published: Jan. 1, 2024

Language: Английский

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Online identification methods for a class of Hammerstein nonlinear systems using the adaptive particle filtering

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 186, P. 115181 - 115181

Published: July 1, 2024

Language: Английский

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Auxiliary Model‐Based Maximum Likelihood Multi‐Innovation Forgetting Gradient Identification for a Class of Multivariable Systems

Optimal Control Applications and Methods, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 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.

Language: Английский

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Sliding Window Iterative Identification for Nonlinear Closed‐Loop Systems Based on the Maximum Likelihood Principle

International Journal of Robust and Nonlinear Control, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

ABSTRACT The parameter estimation problem for the nonlinear closed‐loop systems with moving average noise is considered in this article. For purpose of overcoming difficulty that dynamic linear module and static lead to identification complexity issues, unknown parameters from both modules are included a vector by use key term separation technique. Furthermore, an sliding window maximum likelihood least squares iterative algorithm gradient derived estimate parameters. numerical simulation indicates efficiency proposed algorithms.

Language: Английский

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Multi‐Innovation Gradient Identification Methods for Bilinear Output‐Error Systems

Optimal Control Applications and Methods, Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

ABSTRACT This article addresses the parameter estimation problems of bilinear output‐error systems, and auxiliary model identification idea particle filtering technique are adopted to overcome obstacle resulting from unknown true outputs. Then a filtering‐based forgetting factor stochastic gradient algorithm is proposed for systems. To enhance convergence rate accuracy estimation, we expand scalar innovation an vector develop multi‐innovation algorithm. Finally, numerical example practical continuous stirred tank reactor process provided show that discussed methods work well. The results indicate algorithms effective identifying systems can generate more accurate estimates than model‐based

Language: Английский

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Highly Efficient Two-Stage Filtering-Based Maximum Likelihood Stochastic Gradient Algorithm for Multiple-Input Multiple-Output Systems

Circuits Systems and Signal Processing, Journal Year: 2025, Volume and Issue: unknown

Published: March 22, 2025

Language: Английский

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Highly efficient maximum-likelihood identification methods for bilinear systems with colored noises

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering, Journal Year: 2024, Volume and Issue: 238(10), P. 1763 - 1784

Published: July 27, 2024

This paper mainly discussed the highly efficient iterative identification methods for bilinear systems with autoregressive moving average noise. Firstly, input-output representation of is derived through eliminating unknown state variables in model. Then based on maximum-likelihood principle, a gradient-based (ML-GI) algorithm proposed to identify parameters colored noises. For improving computational efficiency, original model divided into three sub-identification models smaller dimensions and fewer parameters, hierarchical (H-ML-GI) by using principle. A (GI) given comparison. Finally, algorithms are verified simulation example practical continuous stirred tank reactor (CSTR) example. The results show that effective identifying noises H-ML-GI has higher efficiency faster convergence rate than ML-GI GI algorithm.

Language: Английский

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Identification of a Non‐Commensurate Fractional‐Order Nonlinear System Based on the Separation Scheme

International Journal of Adaptive Control and Signal Processing, Journal Year: 2024, Volume and Issue: 39(1), P. 116 - 131

Published: Oct. 27, 2024

ABSTRACT This article is aimed to study the parameter estimation problems of a non‐commensurate fractional‐order system with saturation and dead‐zone nonlinearity. In order reduce structural complexity system, model separation scheme used decompose nonlinear into two subsystems, one includes parameters linear part other part. Then, we derive an auxiliary separable gradient‐based iterative algorithm help scheme. addition, improve utilization real time information, multi‐innovation presented based on sliding measurement window. Finally, feasibility algorithms validated by numerical simulations.

Language: Английский

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Iterative parameter estimation for a class of fractional-order Hammerstein nonlinear systems disturbed by colored noise

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

Considering the existence of nonlinearity and fractional-order phenomena in practical environments, this paper investigates parameter estimation methods for a class Hammerstein nonlinear systems disturbed by colored noise. The are based on decomposition strategy, separating identification from system parameters. Meanwhile, is divided into two subsystems, which linear block using hierarchical principle. To overcome problem redundant estimation, over-parameterization method key item separation used, respectively. Then, two-stage gradient-based iterative algorithm term derived, auxiliary model used to compute unmeasurable variables. In addition, we analyze computational efficiencies proposed algorithms. simulation results indicate that algorithms effective. Finally, evaluated through battery model. show well agreement with real outputs.

Language: Английский

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Hierarchical Least Squares Identification for the Multivariate Input Nonlinear Controlled Autoregressive Moving Average Systems

International Journal of Adaptive Control and Signal Processing, Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

ABSTRACT This article presents a decomposition‐based least squares estimation algorithm for the multivariate input nonlinear system. By using hierarchical identification principle, breaks down system into two subsystems, one containing parameters of linear dynamic block and other static block. Treating unknown variables contained in information vector model is to replace them with outputs an auxiliary model. The comparative results between recursive developed this are provided test proposed algorithms have lower computational cost higher accuracy. Furthermore, convergence analyzed, which can guarantee stability algorithm. simulation confirm efficacy derived effectively estimating systems.

Language: Английский

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