Adaptive non-linear high gain observer based sensorless speed estimation of an induction motor

Kadrine, Abderrahmane; Tir, Zoheir; Malik, O.P.; Hamida, Mohamed Assaad; Reatti, Alberto; Houari, Azeddine

doi:10.1016/j.jfranklin.2020.06.013

Cited by 20 publications

(17 citation statements)

References 61 publications

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“…This results in lowered robustness. A similar observation is made for a wider class of observers in [26].…”

Section: Introductionsupporting

confidence: 79%

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Developing Induction Motor State Observers with Increased Robustness

Białoń¹,

Pasko²,

Niestrój³

2020

Energies

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This paper presents the results of recently conducted research on Luenberger observers with non-proportional feedbacks. The observers are applied for the reconstruction of magnetic fluxes of an induction motor. Structures of the observers known from the control theory are presented. These are a proportional observer, a proportional-integral observer, a modified integral observer, and an observer with additional integrators. The practical application of some of these observers requires modifications to their structures. In the paper, the simulation results for all mentioned types of observers are presented. The simulations are performed with a Scilab-Xcos model which is attached to this paper. The problem of gains selection of the observers is discussed. Gains are selected with the described optimization method based on a genetic algorithm. A Scilab file launching the genetic algorithm also is attached to this paper.

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“…This results in lowered robustness. A similar observation is made for a wider class of observers in [26].…”

Section: Introductionsupporting

confidence: 79%

“…In particular, unlike some deterministic optimization algorithms, it does not require knowledge of its derivative value. Computing the derivative of the fitness function would be troublesome in the present case due to the conditional structure of the components (21), (22), (26), (27) and (29).…”

Section: Of 24mentioning

confidence: 99%

Developing Induction Motor State Observers with Increased Robustness

Białoń¹,

Pasko²,

Niestrój³

2020

Energies

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“…To implement the MTPA approach, the current i d is pushed to go with the reference calculated in (12). Consider the next sliding surface…”

Section: Ipmsm Controller Designmentioning

confidence: 99%

“…This observer is a good candidate to compensate the non-modelled dynamics; however, it is very sensitive to the measurement noises. To deal with this limitation, an adaptive version of this observer has been recently employed for self-sensing control of the induction machine [12]. Thanks to their robustness, the sliding mode observers are frequently used for speed and position estimation of electrical motors [13].…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Self-Sensing Control of Synchronous Machine Using Super Twisting Interconnected Observers

et al. 2020

Self Cite

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The aim of this study is to propose a self-sensing control of internal permanent-magnet synchronous machines (IPMSMs) based on new high order sliding mode approaches. The high order sliding mode control will be combined with the backstepping strategy to achieve global or semi global attraction and ensure finite time convergence. The proposed control strategy should be able to reject the unmatched perturbations and reject the external perturbation. On the other hand, the super-twisting algorithm will be combined with the interconnected observer methodology to propose the multi-input–multi-output observer. This observer will be used to estimate the rotor position, the rotor speed and the stator resistance. The proposed controller and observer ensure the finite-time convergence to the desired reference and measured state, respectively. The obtained results confirm the effectiveness of the suggested method in the presence of parametric uncertainties and unmeasured load torque at various speed ranges.

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“…In [24], the unmeasured states and the unknown inputs have been estimated by designing cascaded high-gain observers. Recently, in [25], an adaptive nonlinear high-gain observer is proposed to estimate the speed of an induction motor.…”

Section: Introductionmentioning

confidence: 99%

Robust State Estimation for a Nonlinear Hybrid Model of the Alternating Activated Sludge Process Using Filtered High-Gain Observers

Boudagga

Dimassi

Hadj-Said

et al. 2020

Mathematical Problems in Engineering

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In this paper, a robust state estimation method based on a filtered high-gain observer is developed for the alternating activated sludge process (AASP) considered as a nonlinear hybrid system. Indeed, we assume that the biodegradable substrate and the ammonia concentrations in the AASP model are unmeasured due to the high cost of their sensors whose maintenance is also very expensive. The observer design is based on the association of the classical high-gain observer and the idea of the application of linear filters on the observation error to deal with measurement noise. It is shown through a Lyapunov analysis that the designed observer ensures the estimation of the unmeasured states (the biodegradable substrate and the ammonia concentrations) based on the measured dissolved oxygen and nitrate concentrations subject to noise. A comparison with the classical high-gain observer is performed via numerical simulations in order to show the robustness of the suggested estimation approach against Gaussian measurement noise.

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Adaptive non-linear high gain observer based sensorless speed estimation of an induction motor

Cited by 20 publications

References 61 publications

Developing Induction Motor State Observers with Increased Robustness

Developing Induction Motor State Observers with Increased Robustness

Sliding Mode Self-Sensing Control of Synchronous Machine Using Super Twisting Interconnected Observers

Robust State Estimation for a Nonlinear Hybrid Model of the Alternating Activated Sludge Process Using Filtered High-Gain Observers

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