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Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive.

Gutierrez-Villalobos JM, Rodriguez-Resendiz J, Rivas-Araiza EA, Martínez-Hernández MA - Sensors (Basel) (2015)

Bottom Line: These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes.The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime.Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

View Article: PubMed Central - PubMed

Affiliation: Laboratorio de Mecatrónica, Universidad Autónoma de Querétaro, Cerro de las Campanas, Col. Las Campanas, S/N, Queretaro 76010, Mexico. marcelino.gutierrez@uaq.mx.

ABSTRACT
Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

No MeSH data available.


Cerebellar model articulation controller (CMAC)-adaptive linear neuron (ADALINE) structure for the parameter estimator.
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sensors-15-15311-f002: Cerebellar model articulation controller (CMAC)-adaptive linear neuron (ADALINE) structure for the parameter estimator.

Mentions: In this algorithm, a cerebellar model articulation controller (CMAC) was used to estimate ωr, it is selected for its capability of quickly learning non-linear functions due to the local nature of its weight modification. These networks are simple to implement when compared to other types of neural network as describe in [9]. Then, for Rr estimation, a block with an adaptive linear neuron (ADALINE) structure is selected. The main advantages of this ANN are its simplicity and the ability to be trained online; besides, the ADALINE weights can be interpreted physically, as explained in [10]. Figure 2 presents the proposed ANN scheme.


Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive.

Gutierrez-Villalobos JM, Rodriguez-Resendiz J, Rivas-Araiza EA, Martínez-Hernández MA - Sensors (Basel) (2015)

Cerebellar model articulation controller (CMAC)-adaptive linear neuron (ADALINE) structure for the parameter estimator.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541832&req=5

sensors-15-15311-f002: Cerebellar model articulation controller (CMAC)-adaptive linear neuron (ADALINE) structure for the parameter estimator.
Mentions: In this algorithm, a cerebellar model articulation controller (CMAC) was used to estimate ωr, it is selected for its capability of quickly learning non-linear functions due to the local nature of its weight modification. These networks are simple to implement when compared to other types of neural network as describe in [9]. Then, for Rr estimation, a block with an adaptive linear neuron (ADALINE) structure is selected. The main advantages of this ANN are its simplicity and the ability to be trained online; besides, the ADALINE weights can be interpreted physically, as explained in [10]. Figure 2 presents the proposed ANN scheme.

Bottom Line: These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes.The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime.Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

View Article: PubMed Central - PubMed

Affiliation: Laboratorio de Mecatrónica, Universidad Autónoma de Querétaro, Cerro de las Campanas, Col. Las Campanas, S/N, Queretaro 76010, Mexico. marcelino.gutierrez@uaq.mx.

ABSTRACT
Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

No MeSH data available.