Hierarchical recursive least squares parameter estimation methods for multiple‐input multiple‐output systems by using the auxiliary models

Xing, Haoming; Ding, Feng; Pan, Feng; Yang, Erfu

doi:10.1002/acs.3669

Cited by 36 publications

(1 citation statement)

References 118 publications

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“…Finally, the effectiveness of the proposed algorithms is tested on MatLab simulation platform. The proposed recursive identification algorithms for the CARMA model can integrate various techniques and mathematical tools [134][135][136][137][138][139][140][141][142] to effectively model industrial processes, network systems, and other complex systems [143][144][145][146][147][148][149][150][151] and so on.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical estimation methods based on the penalty term for controlled autoregressive systems with colored noises

Sun,

Xiong,

Ding

et al. 2024

Intl J Robust & Nonlinear

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This article considers the parameter estimation problems for the controlled autoregressive systems interfered by moving average noises. A recursive extended gradient algorithm with penalty term is proposed by using the penalty criterion function. By introducing three fictitious output variables, the original system can be decomposed into three subsystems based on the hierarchical principle. The hierarchical recursive extended gradient algorithm with penalty term is then proposed to achieve the parameter estimation. Finally, the experimental results demonstrate the effectiveness of the proposed algorithms.

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Section: Discussionmentioning

confidence: 99%

Hierarchical estimation methods based on the penalty term for controlled autoregressive systems with colored noises

Sun,

Xiong,

Ding

et al. 2024

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

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Iterative parameter identification for Hammerstein systems with ARMA noises by using the filtering identification idea

Bedoui,

Abderrahim,

Ding

2024

Adaptive Control & Signal

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SummaryIn practical applications, many processes have nonlinear characteristics that require nonlinear models for accurate description. However, constructing such models and determining their parameters are a challenging task. This article explores filtered identification methods for estimating the parameters of a particular type of nonlinear Hammerstein systems with ARMA noise. An auxiliary model‐based least squares algorithm is developed for such systems based on the auxiliary model identification idea. A hierarchical least squares algorithm that utilizes the hierarchical identification principle is proposed to enhance computational efficiency. Additionally, a key term separation technique is employed to express the system output as a linear combination of parameters, allowing the system to be decomposed into smaller subsystems for more efficient estimation of parameters. Simulation results demonstrate the effectiveness of these proposed algorithms.

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Parameter estimation methods for time‐invariant continuous‐time systems from dynamical discrete output responses based on the Laplace transforms

Ibrahim,

Ding

2024

Adaptive Control & Signal

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SummaryIn industrial process control systems, parameter estimation is crucial for controller design and model analysis. This article examines the issue of identifying parameters in continuous‐time models. This article presents a stochastic gradient estimation algorithm and a recursive least squares estimation algorithm for identifying the parameters of continuous systems. It derives the parameter identification model of linear continuous‐time systems based on the Laplace transforms of the input and output of the systems. To prove that the techniques given here work, we have included a simulated example.

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Hierarchical recursive least squares parameter estimation methods for multiple‐input multiple‐output systems by using the auxiliary models

Cited by 36 publications

References 118 publications

Hierarchical estimation methods based on the penalty term for controlled autoregressive systems with colored noises

Hierarchical estimation methods based on the penalty term for controlled autoregressive systems with colored noises

Iterative parameter identification for Hammerstein systems with ARMA noises by using the filtering identification idea

Parameter estimation methods for time‐invariant continuous‐time systems from dynamical discrete output responses based on the Laplace transforms

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