Application of the Newton iteration algorithm to the parameter estimation for dynamical systems
TLDR
Simulation results show that the obtained models can capture the dynamics of the systems, i.e., the estimated model's outputs are close to the outputs of the actual systems.About:
This article is published in Journal of Computational and Applied Mathematics.The article was published on 2015-11-01 and is currently open access. It has received 153 citations till now. The article focuses on the topics: Dynamical systems theory & Step response.read more
Citations
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Journal ArticleDOI
A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems
TL;DR: A filtering based extended stochastic gradient algorithm and a filtering based multi-innovation ESG algorithm for improving the parameter estimation accuracy for a multivariable system with moving average noise.
Journal ArticleDOI
The damping iterative parameter identification method for dynamical systems based on the sine signal measurement
TL;DR: A damping parameter estimation algorithm for dynamical systems based on the sine frequency response is proposed and a damping factor is introduced in the proposed iterative algorithm in order to overcome the singular or ill-conditioned matrix during the iterative process.
Journal ArticleDOI
Control Algorithms of Magnetic Suspension Systems Based on the Improved Double Exponential Reaching Law of Sliding Mode Control
Jian Pan,Wei Li,Haipeng Zhang +2 more
TL;DR: The improved algorithm has better control performances than the traditional SMC and the power reaching law integral SMC algorithm, such as less chattering, smaller overshoots, and faster response speed.
Journal ArticleDOI
State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors
TL;DR: This paper proposes a state filtering method for the single-input–single-output bilinear systems by minimizing the covariance matrix of the state estimation errors by extending the extended Kalman filter algorithm to multiple- input–multiple-output Bilinear Systems.
Journal ArticleDOI
Hierarchical Parameter Estimation for the Frequency Response Based on the Dynamical Window Data
TL;DR: In this paper, a hierarchical multi-innovation stochastic gradient estimation method is derived through parameter decomposition, and the forgetting factor and the convergence factor are introduced to improve the performance of the algorithm.
References
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Journal ArticleDOI
Gradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principle
Feng Ding,Xinggao Liu,Jian Chu +2 more
TL;DR: The simulation results confirm that the proposed two algorithms can give satisfactory identification accuracies and the least-squares-based iterative algorithm has faster convergence rates than the gradient-basedIterative algorithm.
Journal ArticleDOI
Least squares based and gradient based iterative identification for Wiener nonlinear systems
Dongqing Wang,Feng Ding +1 more
TL;DR: This paper derives a least squares-based and a gradient-based iterative identification algorithms for Wiener nonlinear systems, estimating directly the parameters of Wiener systems without re-parameterization to generate redundant estimates.
Journal ArticleDOI
Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration
Ling Xu,Lei Chen,Weili Xiong +2 more
TL;DR: In this article, a new Newton iterative identification method is presented for estimating the parameters of a second-order dynamic system utilizing the obtained data from the step response, in order to obtain the desired dynamic performance, a controller design method based on the root locus is presented to meet the requirement of the dynamic performance of the overshoot.
Journal ArticleDOI
An efficient hierarchical identification method for general dual-rate sampled-data systems
Yanjun Liu,Feng Ding,Yang Shi +2 more
TL;DR: The proposed D-LS algorithm does not require computing the covariance matrices with large sizes and matrix inverses in each recursion step, and thus has a higher computational efficiency than the RLS algorithm.
Journal ArticleDOI
Combined state and least squares parameter estimation algorithms for dynamic systems
TL;DR: The parameter estimation algorithm of establishing the mathematical models for dynamic systems is discussed and an estimated states based recursive least squares algorithm is presented, and the states of the system are computed through the Kalman filter using the estimated parameters.
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Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration
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