Nonlinear systems time-varying parameter estimation: Application to induction motors

Kenné, Godpromesse; Ahmed‐Ali, Tarek; Lamnabhi‐Lagarrigue, Françoise; Arzandé, Amir

doi:10.1016/j.epsr.2008.03.014

Cited by 55 publications

(27 citation statements)

References 8 publications

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“…What is more, the critical parameters may greatly vary during control operations, namely the load torque, the stator resistance and rotor resistance. In fact, their significant impact on the system dynamics is well documented in the literature (Kenn et al, 2008(Kenn et al, , 2011Chehimi et al, 2011;Barut and Bogosyan, 2005;Hadj Said et al, 2011). Concerning R r , it can be doubled because of heating.…”

Section: Introductionmentioning

confidence: 93%

Adaptive interconnected observer-based sensorless non-linear generalised predictive control for IMs with parameters tuning

Chehimi

Said

M’Śahli

2013

IJAAC

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The combination of the non-linear generalised predictive control (NGPC) and the adaptive interconnected observer (AIO) is considered for the design of the output feedback predictive controller. Since the induction motors (IMs) are intensively used in applications requiring high dynamic performance, the algorithm developed is potentially useful for a fully decoupling in speed and flux magnitude regulation. Indeed, in a first step the state-feedback controller is given, which stems from the approximation of the output tracking error in the receding horizon by its Taylor-series expansion is given. In a second step, the AIO property is exploited in order to avoid the problem of parameters uncertainly and state unavailability during the drive motor, with realising a suitable tracking of trajectories for the system output. This results in a sensorless control of the induction motors, whose rotor and stator resistances, the load torque, the rotor flux and mechanical speed are simultaneously recovered by profiting only from the measurements of the stator current. Effectiveness of the proposed observers and the performance of the controller are confirmed by simulation results.

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Section: Introductionmentioning

confidence: 93%

Adaptive interconnected observer-based sensorless non-linear generalised predictive control for IMs with parameters tuning

Chehimi

Said

M’Śahli

2013

IJAAC

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“…e q ∈ R m is an unknown bounded real valued function that depend on uncertain parameters. According to the appropriate choice already proposed, easy to see the following expression for our induction motor system [1]:…”

Section: Model Of the Induction Motormentioning

confidence: 99%

Apaptative high gain observer based a sensorless output feedback controller for IMs with time-varying load torque and rotor resistance

Chehimi

Said

M’Śahli

2012

2012 16th IEEE Mediterranean Electrotechnical Conference

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This work highlights the link between the accurate knowledge of some critical parameters, such as rotor resistance and load torque, and its important role in correct speed sensorless induction motor control. Indeed, the main characteristics of the output feedback predictive controller lie in the fact that their design is made by exploiting on-line the model at each sampling instant. In the spirit to achieve high performance control, the incorporation of an observer is proposed for recovering online all machine variables (the internal states and time varying parameters). Simulation results for IMs are addressed to show the effectiveness of the control design method.

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“…There are already scholars use different methods to research the rotor resistance, among which, reference [1] presents an algorithm for time-varying parameter estimation, it is simple and easily implementable in real-time, The application of this approach to on-line estimation of the rotor resistance of IM shows a rapidly converging estimate in spite of measurement noise, discretization effects, parameter uncertainties and modeling inaccuracies. Reference [2] presents a fuzzy logic MRAS method with 3 realization form, the reference and adaptive model are deduced form different models, they all depend on Popov's hyperstatic theory, the acquisition of reference model always have difficulties.…”

Section: Introductionmentioning

confidence: 98%

“…1 represents the weight between the number i input and number j hidden layer neuron, the weight between the number j hidden layer neuron and number k output layer neuron, the weight feedback to the number j hidden layer neuron. 1 …”

mentioning

confidence: 99%

Motor rotor resistance identification based on Elman neural network

Fan

Guang-hui

et al. 2012

2012 IEEE International Conference on Automation and Logistics

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Motor parameter identification is problem must be faced by high performance variable frequency speed adjustment system include Vector Control. Explore new effective parameter identification method possess vast theoretical and practical meanings. Motor's mathematical model has the character of high order, nonlinear and complicate coupling, the parameter change with the work state is difficult to describe with a definite function. Rotor resistance is identified with Elman neural network which has the ability of function approximation and unique feedback. The simulation result is validated by the parameter obtained with other methods and shows some advantages. It has some reference meaning to more extend motor parameter identification.

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Nonlinear systems time-varying parameter estimation: Application to induction motors

Cited by 55 publications

References 8 publications

Adaptive interconnected observer-based sensorless non-linear generalised predictive control for IMs with parameters tuning

Adaptive interconnected observer-based sensorless non-linear generalised predictive control for IMs with parameters tuning

Apaptative high gain observer based a sensorless output feedback controller for IMs with time-varying load torque and rotor resistance

Motor rotor resistance identification based on Elman neural network

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