Parameter Estimation and Model-free Multi-innovation Adaptive Control Algorithms

International Journal of Control Automation and Systems, Journal Year: 2024, Volume and Issue: 22(11), P. 3509 - 3524

Published: Nov. 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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Auxiliary model‐based recursive least squares and stochastic gradient algorithms and convergence analysis for feedback nonlinear output‐error systems

International Journal of Adaptive Control and Signal Processing, Journal Year: 2024, Volume and Issue: 38(10), P. 3268 - 3289

Published: July 29, 2024

Summary This paper deals with the problem of parameter estimation for feedback nonlinear output‐error systems. The auxiliary model‐based recursive least squares algorithm and stochastic gradient are derived estimation. Based on process theory, convergence proposed algorithms proved. simulation results indicate that can estimate parameters systems effectively.

Language: Английский

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Separable Synchronous Gradient‐Based Iterative Algorithms for the Nonlinear ExpARX System

International Journal of Adaptive Control and Signal Processing, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 9, 2024

ABSTRACT This article is aimed to study the parameter identification of ExpARX system. To overcome computational complexity associated with a large number feature parameters, separation scheme based on different features model introduced. In terms phenomenon that coupling parameters lead inability algorithms, separable synchronous interactive estimation method introduced eliminate and perform in accordance hierarchical principle. For purpose achieving high‐accuracy performance reducing complexity, gradient iterative algorithm derived by means search. order improve accuracy, multi‐innovation proposed introducing theory. convergence speed, conjugate Finally, simulation example real‐life piezoelectric ceramics are used verify effectiveness algorithm.

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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Auxiliary model maximum likelihood least squares-based iterative algorithm for multivariable autoregressive output-error autoregressive moving average systems

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 16, 2024

Identification of multivariable systems is great significance to control systems. This paper focuses on the parameter identification problems for autoregressive output-error moving average (M-AROEARMA) On basis decomposition strategy, M-AROEARMA model de- composed into multiple subsystem models. By means auxiliary idea, least squares-based iterative algorithm derived. For purpose achieving highly accurate performance under colored noises interference, an maximum likelihood proposed by utilizing principle. The numerical simulation example demonstrates effectiveness algorithms.

Language: Английский

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Identification of Linear Systems Having Time Delay Connected in Series

WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL, Journal Year: 2024, Volume and Issue: 19, P. 234 - 239

Published: Aug. 13, 2024

Nonlinear system identification has been a hot research field over the past two decades. A substantial portion of work carried out based on block-structured models. Time delay is problem occurring in most industrial applications. The time can destabilize system. Then, latter should be determined to control This aims present an approach allowing linear having connected series. In this study, method proposed determine parameters. sine inputs / or periodic stepwise input.

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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