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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Highly efficient three-stage maximum likelihood recursive least squares identification method for multiple-input multiple-output systems

Systems & Control Letters, Journal Year: 2025, Volume and Issue: 200, P. 106094 - 106094

Published: April 6, 2025

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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Adaptive neural network course tracking control of USV with input quantisation and output constraints

International Journal of Systems Science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 15

Published: Feb. 18, 2025

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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Hierarchical Newton iterative identification methods for a class of input multi-piecewise Hammerstein models with autoregressive noise

Mathematics and Computers in Simulation, Journal Year: 2025, Volume and Issue: 237, P. 247 - 262

Published: April 25, 2025

Language: Английский

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A novel filtering-based recursive identification method for a fractional-order Hammerstein state space system with piecewise nonlinearity

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

Published: Jan. 28, 2025

This paper investigates parameters and states estimation for a class of fractional-order state space systems with colored noises. To provide accurate parameter estimation, we suggest novel gradient descent algorithm based on the extended Kalman filtering. The new approach features lower error variances faster convergence rate than conventional algorithm. A data filtering is introduced to filter input output data, thereby reducing impact noises accuracy estimates.

Language: Английский

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Robust recursive estimation for the errors-in-variables nonlinear systems with impulsive noise

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 19, 2025

The non-Gaussian characteristic of the external disturbance poses a great challenge for system modeling and identification. This paper develops robust recursive estimation algorithm errors-in-variables nonlinear with impulsive noise. is formulated by minimizing continuous logarithmic mixed p-norm criterion, capable giving against noise through an adjustable weight gain. monomials noisy input are estimated expressions based on bias correction. Furthermore, hierarchical derived to reduce computational loads. simulation studies demonstrate feasibility proposed algorithms.

Language: Английский

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Adaptive fuzzy event-triggered fast fixed-time filtering backstepping formation control for underactuated USVs with LOS range and bearing angle constraints

Ocean Engineering, Journal Year: 2025, Volume and Issue: 325, P. 120674 - 120674

Published: Feb. 27, 2025

Language: Английский

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