Data Filtering-Based Maximum Likelihood Gradient-Based Iterative Algorithm for Input Nonlinear Box–Jenkins Systems with Saturation Nonlinearity

Circuits Systems and Signal Processing, Год журнала: 2024, Номер 43(11), С. 6874 - 6910

Опубликована: Авг. 1, 2024

Язык: Английский

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Auxiliary model maximum likelihood gradient‐based iterative identification for feedback nonlinear systems

Optimal Control Applications and Methods, Год журнала: 2024, Номер 45(5), С. 2346 - 2363

Опубликована: Июнь 17, 2024

Abstract This article considers the iterative identification problems for a class of feedback nonlinear systems with moving average noise. The model contains both dynamic linear module and static module, which brings challenges to identification. By utilizing key term separation technique, unknown parameters from modules are included in parameter vector. Furthermore, an auxiliary maximum likelihood gradient‐based algorithm is derived estimate parameters. In addition, stochastic gradient as comparison. numerical simulation results indicate that can effectively get more accurate estimates than algorithm.

Язык: Английский

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

International Journal of Control Automation and Systems, Год журнала: 2024, Номер 22(11), С. 3509 - 3524

Опубликована: Ноя. 1, 2024

Язык: Английский

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

International Journal of Adaptive Control and Signal Processing, Год журнала: 2024, Номер 38(9), С. 3134 - 3160

Опубликована: Июнь 28, 2024

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

Язык: Английский

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

International Journal of Adaptive Control and Signal Processing, Год журнала: 2024, Номер 38(9), С. 3213 - 3232

Опубликована: Июль 3, 2024

Summary In 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. presents a stochastic gradient algorithm recursive least squares continuous systems. It derives identification linear systems based on Laplace transforms input output To prove that techniques given here work, we have included simulated example.

Язык: 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, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 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.

Язык: 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, Год журнала: 2025, Номер unknown

Опубликована: Март 22, 2025

Язык: Английский

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Gradient-Based Recursive Parameter Estimation Methods for a Class of Time-Varying Systems from Noisy Observations

Circuits Systems and Signal Processing, Год журнала: 2024, Номер 43(11), С. 7089 - 7116

Опубликована: Июль 29, 2024

Язык: 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, Год журнала: 2024, Номер 38(10), С. 3268 - 3289

Опубликована: Июль 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.

Язык: Английский

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

International Journal of Adaptive Control and Signal Processing, Год журнала: 2024, Номер unknown

Опубликована: Сен. 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.

Язык: Английский

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