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

Xu, Ling; Chen, Lei; Xiong, Weili

doi:10.1007/s11071-014-1801-7

Cited by 223 publications

(82 citation statements)

References 52 publications

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“…The identification steps of the F-GI algorithm with finite measurement data in (43), (44), (45), (46), (47), (48), (49), (50), (51), (52), (53), (54), (55), (56), (57), and (58) to compute θ s,k and θ n,k are listed as follows.…”

Section: Start Collect {U(t) Y(t)mentioning

confidence: 99%

Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Pan

Jiang

et al. 2018

Complexity

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The identification problem of multivariable controlled autoregressive systems with measurement noise in the form of the moving average process is considered in this paper. The key is to filter the input-output data using the data filtering technique and to decompose the identification model into two subidentification models. By using the negative gradient search, an adaptive data filtering-based gradient iterative (F-GI) algorithm and an F-GI with finite measurement data are proposed for identifying the parameters of multivariable controlled autoregressive moving average systems. In the numerical example, we illustrate the effectiveness of the proposed identification methods.

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Section: Start Collect {U(t) Y(t)mentioning

confidence: 99%

Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Pan

Jiang

et al. 2018

Complexity

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“…System identification studies mathematical models of dynamic systems by fitting experimental data to a suitable model structure [1,2]. Many practical systems have multiple inputs and multiple outputs such as chemical processes [3,4], automation devices [5][6][7], and network communication engineering [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Ding

2017

Complexity

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This paper considers the identification problem of multi-input-output-error autoregressive systems. A hierarchical gradient based iterative (H-GI) algorithm and a hierarchical least squares based iterative (H-LSI) algorithm are presented by using the hierarchical identification principle. A gradient based iterative (GI) algorithm and a least squares based iterative (LSI) algorithm are presented for comparison. The simulation results indicate that the H-LSI algorithm can obtain more accurate parameter estimates than the LSI algorithm, and the H-GI algorithm converges faster than the GI algorithm.

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“…[8][9][10] Compared with the gradient methods, recursive least squares methods take advantage of fast convergence rates, but when the system is contaminated by the colored noises, the performance of these methods, such as estimation accuracy, will decline if the colored noises are not well handled. One way to improve the estimation accuracy under the noise environment is to include the noise information into the information vector.…”

Section: Introductionmentioning

confidence: 99%

Novel recursive least squares identification for a class of nonlinear multiple-input single-output systems using the filtering technique

Wang

Xun

2016

Advances in Mechanical Engineering

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In this article, we consider the identification problem of a class of nonlinear multiple-input single-output output-error autoregressive systems. First, a recursive generalized least squares algorithm using the auxiliary model identification idea is developed. Then, using the filtering technique, the identification model is decomposed into a filtered sub-identification model and a noise sub-identification model. For solving the difficulties that the filter is unknown and the information vectors contain the unknown variables, the interactive estimation theory and the the idea of replacing the unknown variables with their corresponding estimates are employed: the recursive least squares method is again used for identifying the system and noise model parameters, and the parameter estimates of the noise model are used to construct the estimated filter. Finally, a nonlinear example is given to verify the effectiveness of the algorithms, and the simulation results show that the recursive least squares algorithm using the filtering technique can produce more accurate parameter estimates under larger noise variances.

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

Cited by 223 publications

References 52 publications

Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Novel recursive least squares identification for a class of nonlinear multiple-input single-output systems using the filtering technique

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