The filtering-based recursive least squares identification and convergence analysis for nonlinear feedback control systems with coloured noises

Xu, Ling; Xu, Huan; Wei, Chun; Ding, Feng; Zhu, Quanmin

doi:10.1080/00207721.2024.2375615

International Journal of Systems Science

2024

DOI: 10.1080/00207721.2024.2375615

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The filtering-based recursive least squares identification and convergence analysis for nonlinear feedback control systems with coloured noises

Ling Xu,

Huan Xu,

Chun Wei

et al.

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2024

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Cited by 4 publications

References 124 publications

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Q‐Learning Based Adaptive Kalman Filtering With Adaptive Window Length

Tang,

Luan,

Ding

et al. 2024

Adaptive Control & Signal

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In this article, we propose an adaptive Kalman filtering with adaptive window length based on Q‐learning for dynamic systems with unknown model information. The iteration step length of the Q‐function is quantitatively adjusted through the influence function. The adaptive Kalman filtering algorithm is used to set an appropriate weight matrix for the Q‐function to estimate unknown model parameters. One numerical example and a practice‐oriented case are given to illustrate the effectiveness of the proposed method. It is shown that this filtering can provide state estimates of best accuracy among all the compared methods when the model mismatch and noise statistical characteristics change.

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Q‐Learning Based Adaptive Kalman Filtering With Adaptive Window Length

Tang,

Luan,

Ding

et al. 2024

Adaptive Control & Signal

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

Liu,

Li,

Liu

et al. 2024

Intl J Robust & Nonlinear

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

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Separable synchronous auxiliary model adaptive momentum estimation strategy for a time-varying system with colored noise from on-line measurements

Zhao,

2024

ISA Transactions

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The filtering-based recursive least squares identification and convergence analysis for nonlinear feedback control systems with coloured noises

Cited by 4 publications

References 124 publications

Q‐Learning Based Adaptive Kalman Filtering With Adaptive Window Length

Q‐Learning Based Adaptive Kalman Filtering With Adaptive Window Length

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

Separable synchronous auxiliary model adaptive momentum estimation strategy for a time-varying system with colored noise from on-line measurements

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