Identification of closed-loop systems via least-squares method

Zhang, Ying; Feng, Chun-Bo

doi:10.1002/(sici)1099-1115(199802)12:1<29::aid-acs465>3.0.co;2-s

Cited by 4 publications

(4 citation statements)

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“…The proposed vector control system can be completed by a parameter identification method based on the Lyapunov theory [9][10][11][12][13]. In this way, we choose the candidate Lyapunov function as follows:…”

Section: Parameter Identification Algorithmmentioning

confidence: 99%

“…Unfortunately, the performances of the variable structure current controller and the LMC depend on the accurate estimation of the machine parameters, notably the stator and rotor resistances. Here, we investigate a possible one-line identification algorithm of stator and rotor resistances were the operating conditions of the motor are considered using Lyapunov and hyperstability theories [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Modelling and simulation of a double‐star induction machine vector control using copper‐losses minimization and parameters estimation

Mimouni

Dhifaoui²

2002

Adaptive Control & Signal

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This paper shows that it is possible to extend the principle of field-oriented control (FOC) approach to a double-star induction motor (DSIM). In the first stage, a robust variable structure current controller based on space phasor voltages PWM scheme is established. In this current controller design, only the stator currents and rotor speed sensors are used. In the second stage, the FOC method developed for DSIM is motivated by the minimization of the copper losses. The developed approach uses a loss model controller (LMC) and an adaptive rotor flux observer to compute the adequate rotor flux value minimizing the copper losses. The control variables are the stator currents or the machine input power. Compared to the constant rotor flux approach, it is proved that higher performances are achieved. However, the sensitivity of the FOC to parameter error of the machine still remains a problem. To guarantee the performance of the vector control, the stator and rotor resistances are adapted on-line, based on the Lyapunov theory. An appropriate choice of the reference model allows building a Lyapunov function by means of which the updating law can be found. The simulation results show the satisfactory behaviour of the proposed identification algorithm.

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Section: Parameter Identification Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling and simulation of a double‐star induction machine vector control using copper‐losses minimization and parameters estimation

Mimouni

Dhifaoui²

2002

Adaptive Control & Signal

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“…There has been continuing interest in methods of identifying the closed-loop systems [1]- [4]. For example, a modified least-squares algorithm was presented to identify the closed-loop systems in the presence of noises [5]. A hierarchical multi-innovation stochastic gradient algorithm was developed for the identification of a class of closed-loop systems [6].…”

Section: Introductionmentioning

confidence: 99%

“…A hierarchical multi-innovation stochastic gradient algorithm was developed for the identification of a class of closed-loop systems [6]. The algorithms used in [5], [6] are suitable for the closed-loop systems without time-delays. However, timedelays are common in closed-loop engineering systems due to the measurement and transmission.…”

Section: Introductionmentioning

confidence: 99%

A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising

Chen¹,

Liu

Chen

et al. 2020

IEEE Access

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This paper presents a novel method to identify a class of closed-loop systems, in which both the forward channel and the feedback channel have unknown time-delays. Taking into account the time-delays, an overparameterized identification model with a sparse parameter vector is established. Based on the basis pursuit de-noising criterion, the sparse parameter vector is estimated by solving a quadratic programming. The time-delays and the parameters are estimated according to the structure of the parameter estimation vector and the model equivalence principle, respectively. The proposed method is applicable even in the case of a few number of sampled data. The effectiveness of the proposed algorithm is verified by the numerical simulation results. INDEX TERMS Closed-loop system, basis pursuit de-noising, system identification, time-delay estimation.

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Comments on ‘identification of closed-loop systems via least-squares method’

Söderström

1999

Int. J. Adapt. Control Signal Process.

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An error in Reference 1 (Int. J. Adapt. Control Signal Process., 12, 29-39 (1998)) is pointed out. Alternative methods based on instrumental variable estimation are discussed.Reference 14 the spectral properties of the reference signal is not presented. However, some numerical evaluations show that S( , ) has much higher condition number than S (A, B). Hence, there are good reasons to conjecture that the direct IV approach will be more robust and the more accurate alternative, as compared to the indirect approach.

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Identification of closed-loop systems via least-squares method

Cited by 4 publications

References 1 publication

Modelling and simulation of a double‐star induction machine vector control using copper‐losses minimization and parameters estimation

Modelling and simulation of a double‐star induction machine vector control using copper‐losses minimization and parameters estimation

A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising

Comments on ‘identification of closed-loop systems via least-squares method’

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