On-line rotor resistance estimation for an induction motor drive based on DSC

Martinez-Hernandez, Moises Agustin; Mendoza-Mondragón, F.; Reséndiz, Juvenal Rodríguez; Rodríguez-Ponce, Rafael; Gutierrez-Villalobos, J. M.

doi:10.1109/ederc.2012.6532262

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…It order to implement this HANN is necessary to consider current model given by (1), voltage model by (2) is also required to adjust the weights, these equations are shown in the following lines and explained in [6].…”

Section: Hybrid Artificial Neural Network Descriptionmentioning

confidence: 99%

Hybrid artificial neural network for induction motor parameter estimation

Gutierrez-Villalobos

Martinez-Hernandez

Mendoza-Mondragón

et al. 2014

2014 6th European Embedded Design in Education and Research Conference (EDERC)

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Three-phase induction motor electric parameter estimation has been widely used to improve induction motor control performance. A precise match between electrical parameter values and estimated ones is imperative. A value deviation can make induction motor misbehave, which can cause motor overheating even instability. Parameter estimation can be achieved on-line or off-line way with a large number of methods developed to calculate magnetic flux, motor speed, rotor resistance and rotor time constant. These methods include observers, adaptive systems, spectral analysis and artificial intelligence such as neural networks and fuzzy logic. This paper is focused on a hybrid neural network proposed to obtain rotor resistance and speed values, using Texas Instrument development tools to improve a sensorless vector control scheme an improve motor performance.

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Section: Hybrid Artificial Neural Network Descriptionmentioning

confidence: 99%

Hybrid artificial neural network for induction motor parameter estimation

Gutierrez-Villalobos

Martinez-Hernandez

Mendoza-Mondragón

et al. 2014

2014 6th European Embedded Design in Education and Research Conference (EDERC)

Self Cite

View full text Add to dashboard Cite

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“…Currently, studies of intelligent systems are used to control the position [ 13 , 14 ]. For example, the work [ 15 ] use a fuzzy system combined with classic control systems to fit the control signal according to the changes in dynamics.…”

Section: Introductionmentioning

confidence: 99%

Self-Tuning Control Using an Online-Trained Neural Network to Position a Linear Actuator

et al. 2022

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Linear actuators are widely used in all kinds of industrial applications due to being devices that convert the rotation motion of motors into linear or straight traction/thrust motion. These actuators are ideal for all types of applications where inclination, lifting, traction, or thrust is required under heavy loads, such as wheelchairs, medical beds, and lifting tables. Due to the remarkable ability to exert forces and good precision, they are used classic control systems and controls of high-order. Still, they present difficulties in changing their dynamics and are designed for a range of disturbances. Therefore, in this paper, we present the study of an electric linear actuator. We analyze the positioning in real-time and attack the sudden changes of loads and limitation range by the control. It uses a general-purpose control with self-tuning gains, which can deal with the essential uncertainties of the actuator and suppress disturbances, as they can change their weights to interact with changing systems. The neural network combined with PID control compensates the simplicity of this type of control with artificial intelligence, making it robust to drastic changes in its parameters. Unlike other similar works, this research proposes an online training network with an advantage over typical neural self-adjustment systems. All of this can also be dispensed with the engine model for its operation. The results obtained show a decrease of 42% in the root mean square error (RMSE) during trajectory tracking and saving in energy consumption by 25%. The results were obtained both in simulation and in real tests.

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