A Modified Multi-innovation Algorithm to Turntable Servo System Identification

Li, Linwei; Zhang, Huanlong; Ren, Xuemei

doi:10.1007/s00034-020-01392-z

Cited by 9 publications

(11 citation statements)

References 32 publications

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“…The MR-MILS algorithm's convergence proof 6,13,41 : Similarly, for algorithms in Equations ( 55)-( 62), define the parameter estimation error vector as follows:…”

Section: Algorithm Name Storage Complexitiesmentioning

confidence: 99%

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Momentum‐innovation recursive least squares identification algorithm for a servo turntable system based on the output error model

Liu

Cheng

Han

et al. 2023

Intl J Robust & Nonlinear

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In this article, the parameter identification problem of the output error model is investigated under the application requirement of online parameter identification of a two-degree-of-freedom servo turntable system. To eliminate the influence of colored noise in the observed output signal of the output error model on the algorithm identification accuracy, the momentum factor is introduced into the innovation term of the recursive least squares algorithm, and the momentum-innovation recursive least squares (MI-RLS) algorithm is proposed.Further, to improve the convergence speed and identification accuracy of the algorithm while avoiding increasing the algorithm complexity significantly, a reframed multi-innovation strategy is introduced, and a momentum reframed multi-innovation least squares (MR-MILS) algorithm is developed. After analyzing the complexity of the proposed algorithms, the convergence performances of the two algorithms are verified using the theory of martingale convergence, and the results show that the reframed multi-innovation strategy can accelerate the convergence speed of the MR-MILS algorithm. The effectiveness of the proposed identification approach is demonstrated via simulation results.

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“…The MR-MILS algorithm's convergence proof 6,13,41 : Similarly, for algorithms in Equations ( 55)-( 62), define the parameter estimation error vector as follows:…”

Section: Algorithm Name Storage Complexitiesmentioning

confidence: 99%

“…4 System identification algorithms have been the most common method to obtain high-accuracy servo turntable models. [4][5][6] To achieve a low-complexity online identification algorithm, this article uses a linear model structure to characterize a servo turntable system.…”

Section: Introductionmentioning

confidence: 99%

Momentum‐innovation recursive least squares identification algorithm for a servo turntable system based on the output error model

Liu

Cheng

Han

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…e multi-innovation technique is an effective tool to enhance the convergence rate of estimation methods. at is why it is joined with different estimation Complexity algorithms such as gradient algorithm and least-square method in its recursive and iterative form [33,[105][106][107][108][109][110][111][112][113]. Particularly, it is well known that recursive gradient algorithm presents a slow convergence rate compared to other estimation approaches [113].…”

Section: Multi-innovation Improved Backpropagation Gradient (Miibpg)mentioning

confidence: 99%

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

et al. 2021

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This paper proposes an innovative identification approach of nonlinear stochastic systems using Hammerstein–Wiener (HW) model with output-error autoregressive (OEA) noise. Two fuzzy systems are suggested for the identification of the input and output nonlinear blocks of a proposed model from given input-output data measurements. In this work, the need for the commonly used assumptions including well-known structure of input and/or output nonlinearities and/or reversible nonlinear output is eliminated by replacing the intermediate variables and noise with their estimates. Four parametric estimation algorithms to identify the proposed fuzzy-type stochastic output-error autoregressive HW (FSOEAHW) model are derived based on backpropagation algorithm and multi-innovation and data filtering identification techniques. The proposed algorithms are improved backpropagation gradient (IBPG) algorithm, multi-innovation IBPG (MIIBPG) algorithm, a data filtering IBPG (FIBPG) algorithm, and a multi-innovation-based FIBPG (MIFIBPG) algorithm. The convergence of the parameter estimation algorithms is studied. The effectiveness of the proposed algorithms is shown by a given simulation example.

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“…In [14], observer/Kalman filter identification (OKID) and bilinear transform discretization (BTD) were proposed, and the results showed that the proposed BTD identification algorithm is simpler and more computationally efficient. In [15], the reconstructed multi-innovative least-squares method was used to identify the turntable servo system by introducing intermediate-step updates and decomposing the innovative update steps. The inverse problem of the covariance matrix can be solved, and the recognition performance can be improved.…”

Section: Introductionmentioning

confidence: 99%

Reverse Nonlinear Sparrow Search Algorithm Based on the Penalty Mechanism for Multi-Parameter Identification Model Method of an Electro-Hydraulic Servo System

et al. 2022

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Aiming at the multi-parameter identification problem of an electro-hydraulic servo system, a multi-parameter identification method based on a penalty mechanism reverse nonlinear sparrow search algorithm (PRN-SSA) is proposed, which transforms the identification problem of a non-linear system into an optimization problem in a high-dimensional parameter space. In the initial stage of the sparrow search algorithm (SSA), the population distribution is not uniform, and the optimization process is easily disturbed by the local optimal solution. First, adopting a reverse learning strategy increases the exploratory nature of individuals in a population, improves population diversity, and prevents premature maturity. Subsequently, a flexible strain mechanism is provided through the nonlinear convergence factor, adaptive weight factor, and golden sine and cosine factor. The introduction of a nonlinear factor fully balances the global search and local development abilities of the algorithm. Finally, a punishment processing mechanism is developed for vigilantes while retaining the population, providing a suitable search scheme for individuals beyond the boundary, and making full use of the value of each sparrow individual. The effectiveness of each improved strategy is verified through simulation experiments with 23 benchmark functions, and the improved algorithm exhibits better robustness. The results of the model parameter identification of the electro-hydraulic servo system show that the method has a high fitting accuracy between the identification model data and the experimental data, and the fitting degree of the identification model exceeds 97.54%, which further verifies the superiority of the improved algorithm and the effectiveness of the proposed identification strategy.

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A Modified Multi-innovation Algorithm to Turntable Servo System Identification

Cited by 9 publications

References 32 publications

Momentum‐innovation recursive least squares identification algorithm for a servo turntable system based on the output error model

Momentum‐innovation recursive least squares identification algorithm for a servo turntable system based on the output error model

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

Reverse Nonlinear Sparrow Search Algorithm Based on the Penalty Mechanism for Multi-Parameter Identification Model Method of an Electro-Hydraulic Servo System

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