Issues Regarding the Tuning of a Minimum Variance Adaptive Controller

Filip, Ioan; Szeidert, Iosif; Proştean, Octavian; Vasar, Cristian

doi:10.1007/978-3-319-62521-8_7

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…A change of the operating point has as consequence a change of the linear model's parameters and the real-time tracking of these can be done by such a parameter estimation algorithm (in this case, the recursive least square estimator-RLS) [12][13][14]. For the case of an induction generator connected to a power system, its complete functionality is described by a nonlinear mathematical model of the seventh order (the d-q model represented by the Park equations) [10,22,23]. The complete model (and the parameter values) of the considered process has already been presented in extenso in one of the author's recent paper [10] and therefore it is not detailed in the present paper (being mentioned only in an Appendix A at the end of the paper).…”

Section: Design Of the Minimum Variance Control Lawmentioning

confidence: 99%

“…First, we consider a classical PID controller. As already mentioned in Chapter 1, an induction generator (integrated into a wind energy conversion system) is a nonlinear process, described by a seventh order nonlinear model (Park's equations) [20][21][22][23][24]. The control of a nonlinear plants using a linear controller is a difficult task (and a classical PID controller is a linear controller).…”

Section: Appl Sci 2019 9 X For Peer Review 10 Of 21mentioning

confidence: 99%

“…In order to validate the proposed control strategy, an induction generator connected to a power system was considered as a controlled plant. An induction generator is a highly nonlinear plant (described by a mathematical model of the seventh order) and its operating point is frequently changing under the action of internal or external disturbances [20][21][22][23][24]. In this case, an electrical load/unload is considered one of the main disturbances whose effect must be rejected by the system.…”

Section: Introductionmentioning

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: Appl Sci 2019 9 X For Peer Review 10 Of 21mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Minimum-Variance Control System with Variable Control Penalty Factor

et al. 2020

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Considerations about Parameters Estimation into a Minimum Variance Control System

2021

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The starting point to design a minimum variance control law consists in identifying a linearized mathematical model (valid around an operating point) of a nonlinear process, respectively the on-line estimation of the parameters of this model. This paper presents a comparative study regarding the estimation of these parameters for the case when the process operates in open-loop, respectively the process is integrated into a closed-loop system specific to a minimum variance control. The comparison is made both analytically (for the general case) and through a validation study (by simulation) particularized for the case of an induction generator integrated into a wind energy conversion system. The main contribution of this paper consists in proving the fact that, in closed-loop (under the constraints imposed by the control law), the process parameters estimates differ from the real ones identified in open-loop (in free operating mode, without constraints). In addition, as a novelty, the paper demonstrates that, in steady-state, the process gain estimates are identical, both in closed-loop and open-loop, even though they are calculated based on different estimates of these linear model parameters. Thus, based on parameters estimates in closed-loop, the parameter estimator rather allows the estimation of the real process gain, although it does not accurately estimate the real values of the linearized model parameters (correctly estimated only in open-loop).

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Issues Regarding the Tuning of a Minimum Variance Adaptive Controller

Cited by 2 publications

References 9 publications

Minimum-Variance Control System with Variable Control Penalty Factor

Minimum-Variance Control System with Variable Control Penalty Factor

Considerations about Parameters Estimation into a Minimum Variance Control System

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