Controller design and robustness analysis for induction machine-based positioning system

Laroche, Édouard; Bonnassieux, Yvan; Abou-Kandil, Hisham; Louis, Jean‐Paul

doi:10.1016/j.conengprac.2003.09.001

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…A brief presentation of the system follows. The reader may refer to (Laroche et al, 2004) for complementary information.…”

Section: Description Of the Systemmentioning

confidence: 99%

“…Assuming that the stator currents are perfectly controlled (î m = i * m andT = T * ), the model of the induction motor with FOC can be obtained as in (Laroche et al, 2004):…”

Section: Linear Fractional Representationmentioning

confidence: 99%

“…In (Cauët et al, 2002), robustness analysis is performed via parameter-dependent Lyapunov functions and applied to the case of feedback linearization control. In (Laroche et al, 2004) a linear fractional model was presented for evaluating robustness of an induction motor with FOC including a flexible link and µ-analysis results were provided. It included parameter variations of both motor and load.…”

Section: Introductionmentioning

confidence: 99%

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Robustness Analysis of Nonlinear Systems – Application to Induction Motor

Laroche

2005

IFAC Proceedings Volumes

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“…A brief presentation of the system follows. The reader may refer to (Laroche et al, 2004) for complementary information.…”

Section: Description Of the Systemmentioning

confidence: 99%

“…Assuming that the stator currents are perfectly controlled (î m = i * m andT = T * ), the model of the induction motor with FOC can be obtained as in (Laroche et al, 2004):…”

Section: Linear Fractional Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robustness Analysis of Nonlinear Systems – Application to Induction Motor

Laroche

2005

IFAC Proceedings Volumes

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“…However, the main drawback of this kind of controller is its sensitivity to the system-parameter variations and load changes [6,7] . Thus, based on loop-shaping technique, a new control technique is proposed [8] to assure the disturbances rejection and lowering of tracking error. Wai and Chang [9] presented an adaptive observation system with an inverse rotor time-constant observer, based on a model reference adaptive system (MRAS) theory.…”

Section: Introductionmentioning

confidence: 99%

State Feedback Tracking Control for Indirect Field- oriented Induction Motor Using Fuzzy Approach

Allouche

Chaabane

Souissi

et al. 2013

Int. J. Autom. Comput.

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This paper deals with the synthesis of fuzzy controller applied to the induction motor with a guaranteed model reference tracking performance. First, the Takagi-Sugeno (T-S) fuzzy model is used to approximate the nonlinear system in the synchronous d-q frame rotating with field-oriented control strategy. Then, a fuzzy state feedback controller is designed to reduce the tracking error by minimizing the disturbance level. The proposed controller is based on a T-S reference model in which the desired trajectory has been specified. The inaccessible rotor flux is estimated by a T-S fuzzy observer. The developed approach for the controller design is based on the synthesis of an augmented fuzzy model which regroups the model of induction machine, fuzzy observer, and reference model. The gains of the observer and controller are obtained by solving a set of linear matrix inequalities (LMIs). Finally, simulation and experimental results are given to show the performance of the observer-based tracking controller.

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“…Robustness of such controlled systems can be demonstrated by means of simulation for several given values of the respective uncertain parameters [8], [5], [3]. Since such simulation results are not sufficient to confirm robustness, the structured singular value tool can be used for robustness analysis against Linear Time-Invariant (LTI) uncertainties [9]. For Linear Time-Varying (LTV) parametric uncertainty, robustness analysis can be based on parameter-dependent Lyapunov functions if the system is dependening affinely on slowly-varying parameters [10], [11].…”

Section: Introductionmentioning

confidence: 99%

IQC-based robust stability analysis for LPV control of doubly-fed induction generators

Tien

Scherer

Scherpen³

2008

2008 10th International Conference on Control, Automation, Robotics and Vision

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Parameters of electrical machines are usually varying with time in a smooth way due to changing operating conditions, such as variations in the machine temperature and/or the magnetic saturation. This paper is concerned with robust stability analysis of controlled Doubly-Fed Induction Generators (DFIGs) that takes into account the time-varying nature of the parameter variations as well as bounds on their rate-of-variations. First, a self-scheduled Linear Parameter Varying (LPV) current controller design for the inner rotor-side loop is presented. The design is based on viewing the mechanical angular speed as an uncertain yet online measurable parameter and on subsuming the problem into the framework of LPV controller synthesis. Then the parameter-dependent model of the machine is transform into a Linear Fractional Representation, which allows to perform a stability analysis based on a specifically chosen set of Integral Quadratic Constraints (IQCs). Some simulation and analysis test results are given to demonstrate the robustness margins that result with this control algorithm. Index Terms-Doubly-fed induction generator (DFIG), linear parameter varying (LPV) systems, slowly time-varying parameter, robust stability, integral quadratic constraints (IQC).

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Controller design and robustness analysis for induction machine-based positioning system

Cited by 7 publications

References 17 publications

Robustness Analysis of Nonlinear Systems – Application to Induction Motor

Robustness Analysis of Nonlinear Systems – Application to Induction Motor

State Feedback Tracking Control for Indirect Field- oriented Induction Motor Using Fuzzy Approach

IQC-based robust stability analysis for LPV control of doubly-fed induction generators

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