The large deviation for the least squares estimator of nonlinear regression model based on WOD errors

Huang, Xu‐Feng; Tang, Xufei; Deng, Xin; Wang, Xuejun

doi:10.1186/s13660-016-1064-6

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…The technical literature shows that this type of relation between the excitation voltage (considered as the plant input variable) and the terminal voltage (as plant output variable) can be enough accurately described by a 4th order linear mathematical with time-varying parameters [12,22,24,25]. This reduced order (4th) is based on several simplifying assumptions regarding the electrical phenomenology, which does not affect the accuracy of the model near an operating point [12,15,26].…”

Section: Design Of the Minimum Variance Control Lawmentioning

confidence: 99%

“…In case of RLS estimator, these estimates depend on the forgetting factor λ. However, the influence of this factor over the control system perturbed by external disturbances is quite low [18,25,27,28]. The influence of this tuning parameter (λ) is much more significant in the case of process with time-varying internal parameters [10,12,18,29].…”

Section: Design Of the Minimum Variance Control Lawmentioning

confidence: 99%

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Minimum-Variance Control System with Variable Control Penalty Factor

et al. 2020

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The present paper proposes (as the main contribution) an additional self-tuning mechanism for an adaptive minimum-variance control system, whose main goal is to extend its functionality for a large value range of unmeasurable perturbations which disturb the controlled process. Through the standard design procedure, a minimum variance controller uses by default an internal self-tuning mechanism based on the process parameter estimates. However, the main parameter which overwhelmingly influences the control performance is the control penalty factor ( ρ ) . This parameter weights the term that describes the control variance in a criterion function whose minimization is the starting point of the control law design. The classical minimum-variance control involves an off-line tuning of this parameter, its value being set as constant throughout the entire operating regime. Based on the measurement of the process output error, the contribution of the proposed strategy consists in a real-time tuning of the control penalty factor, to ensure the stability of the control system, even under conditions of high disturbances. The proposed tuning mechanism adjusts this parameter by implementing a bipositional switching strategy based on a sharp hysteresis loop. Therefore, instead of the standard solution that involves a constant value of the control penalty factor ρ (a priori computed and set), this paper proposes a dual value for this controller parameter. The main objective is to allow the controlled process to operate in a stable fashion even in more strongly disturbed regimes (regimes where the control system becomes unstable and is usually switched off for safety reasons). To validate the proposed strategy, an induction generator integrated into a wind energy conversion system was considered as controlled plant. Operating under the action of strong disturbances (wind gusts, electrical load variations), the extension of safe operating range (thus avoiding the system disengagement) is an important goal of such a control system.

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Section: Design Of the Minimum Variance Control Lawmentioning

confidence: 99%

Section: Design Of the Minimum Variance Control Lawmentioning

confidence: 99%

Minimum-Variance Control System with Variable Control Penalty Factor

et al. 2020

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“…The results on l.s.e. probabilities of large deviations in nonlinear regression models with correlated observations one can find in [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

Large deviations of regression parameter estimate in the models with stationary sub-Gaussian noise

Ivanov¹

2018

Theor. Probability and Math. Statist.

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“…The results on probabilities of large deviations of an l.s.e. in a nonlinear regression model with correlated observations can be found in the works of Ivanov and Leonenko [11], Prakasa Rao [14], Hu [4], Yang and Hu [16], Huang et al [5].…”

Section: Introductionmentioning

confidence: 99%