Hierarchical identification for multivariate Hammerstein systems by using the modified Kalman filter-Reference-Cited by-同舟云学术

Hierarchical identification for multivariate Hammerstein systems by using the modified Kalman filter

Published:2017-02-14 Issue:6 Volume:11 Page:857-869
ISSN:1751-8652
Container-title:IET Control Theory & Applications
language:en
Short-container-title:IET Control Theory & Applications

Author:

Ma Junxia¹²,Xiong Weili¹,Chen Jing¹,Feng Ding¹³

Affiliation:

1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan UniversityWuxi214122People's Republic of China

2. Department of Chemical and Materials EngineeringUniversity of AlbertaEdmontonABCanadaT6G 2G6

3. School of Electrical and Electronic Engineering, Hubei University of TechnologyWuhan430068People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Control and Optimization,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cta.2016.1033

Reference49 articles.

1. Robust adaptive attenuation of unknown periodic disturbances in uncertain multi-input multi-output systems

2. Multi‐input–multi‐output system experimental validation of model‐free control and virtual reference feedback tuning techniques

3. A proportional differential control method for a time-delay system using the Taylor expansion approximation

4. Application of the Newton iteration algorithm to the parameter estimation for dynamical systems

5. Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration

Cited by 81 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Online identification methods for a class of Hammerstein nonlinear systems using the adaptive particle filtering;Chaos, Solitons & Fractals;2024-09

2. Data Filtering-Based Maximum Likelihood Gradient-Based Iterative Algorithm for Input Nonlinear Box–Jenkins Systems with Saturation Nonlinearity;Circuits, Systems, and Signal Processing;2024-08-01

3. Gradient-Based Recursive Parameter Estimation Methods for a Class of Time-Varying Systems from Noisy Observations;Circuits, Systems, and Signal Processing;2024-07-29

4. 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;2024-07-29

5. 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;2024-07-03