EPE Association: EPE-PEMC 2000 - Topic 09b: Neural Network Control

EPE-PEMC 2000 - Topic 09b: Neural Network Control
You are here: EPE Documents > 04 - EPE-PEMC Conference Proceedings > EPE-PEMC 2000 - Conference > EPE-PEMC 2000 - Topic 09: Motion Control > EPE-PEMC 2000 - Topic 09b: Neural Network Control

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   Application of Artificial Neural Network for Speed Control of Asynchronous Motor with Vector Control
By Brandstetter P., Skotnica M. [View]
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Abstract: Presented paper describes application of artificial neural network in control of A.C. drive with vector control. The predictive speed controller consists of one feedforward and one recursive neural network. The weights and tresholds of both neural nets are constructed rather than trained and require no adaption during the control process. Such strategy allows for very efficient implementation of the controller on digital signal processor. The vector control and ANN´ software algorithms are performed with an TMS320C40 DSP.

   Internal Model Control of an Induction Motor Based on Neural Networks
By Denai M.A., Attia S.A. [View]
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Abstract: This paper deals with the design of a robust speed controller for an induction motor using the internal model control technique. The control system structure includes two feedforward neural networks which are trained to approximate the direct and inverse motor dynamics using a simplified model of the drive. The performance of the controller is evaluated for different operating regimes of the drive system.

   Neuro-Optimal Controller for Vector Controlled Induction Motor
By Gaiceanu M., Rosu E., Tataru A.M. [View]
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Abstract: The rotor field oriented induction motor (IM) is controlled at constant flux, the optimal control synthesis consisting of the determination of the statoric three-phase currents system, based on the longitudinal and transversal components of the statoric phasor current. Thus, the obtained linear mathematical model of IM becomes more accessible in view of the adaptive control [1], optimal control techniques [2], and so on. The goals of the authors to the electrical drive have been provided by using a quadratic energetically performance criteria offered by the optimal control theory [2][3][4]. Therefore, as the problem formulation was done with fixed time, the matrix Riccati differential equation [MRDE] must be solved. This task was performed in [5][6], but it is a more expensive time. To overcome this problem it will be shown the approximation of the optimal control solution by using a feed forward neural network (NN) in the paper. To improve the backpropagation learning algorithm, the momentum method and adaptive learning rate were used. The architecture of the NN is presented in this paper. After the successfully tested NN, this could be implemented with an adequate weights connections and bias downloaded in the learning frame, by using a modest digital controller. The optimal control law oriented to the vector controlled induction machine provides dynamic regimes with minimal energy consumption.

EPE-PEMC 2000 - Topic 09b: Neural Network Control
You are here: EPE Documents > 04 - EPE-PEMC Conference Proceedings > EPE-PEMC 2000 - Conference > EPE-PEMC 2000 - Topic 09: Motion Control > EPE-PEMC 2000 - Topic 09b: Neural Network Control
	[return to parent folder]

	Application of Artificial Neural Network for Speed Control of Asynchronous Motor with Vector Control By Brandstetter P., Skotnica M.	[View] [Download]
Abstract: Presented paper describes application of artificial neural network in control of A.C. drive with vector control. The predictive speed controller consists of one feedforward and one recursive neural network. The weights and tresholds of both neural nets are constructed rather than trained and require no adaption during the control process. Such strategy allows for very efficient implementation of the controller on digital signal processor. The vector control and ANN´ software algorithms are performed with an TMS320C40 DSP.

	Internal Model Control of an Induction Motor Based on Neural Networks By Denai M.A., Attia S.A.	[View] [Download]
Abstract: This paper deals with the design of a robust speed controller for an induction motor using the internal model control technique. The control system structure includes two feedforward neural networks which are trained to approximate the direct and inverse motor dynamics using a simplified model of the drive. The performance of the controller is evaluated for different operating regimes of the drive system.

	Neuro-Optimal Controller for Vector Controlled Induction Motor By Gaiceanu M., Rosu E., Tataru A.M.	[View] [Download]
Abstract: The rotor field oriented induction motor (IM) is controlled at constant flux, the optimal control synthesis consisting of the determination of the statoric three-phase currents system, based on the longitudinal and transversal components of the statoric phasor current. Thus, the obtained linear mathematical model of IM becomes more accessible in view of the adaptive control [1], optimal control techniques [2], and so on. The goals of the authors to the electrical drive have been provided by using a quadratic energetically performance criteria offered by the optimal control theory [2][3][4]. Therefore, as the problem formulation was done with fixed time, the matrix Riccati differential equation [MRDE] must be solved. This task was performed in [5][6], but it is a more expensive time. To overcome this problem it will be shown the approximation of the optimal control solution by using a feed forward neural network (NN) in the paper. To improve the backpropagation learning algorithm, the momentum method and adaptive learning rate were used. The architecture of the NN is presented in this paper. After the successfully tested NN, this could be implemented with an adequate weights connections and bias downloaded in the learning frame, by using a modest digital controller. The optimal control law oriented to the vector controlled induction machine provides dynamic regimes with minimal energy consumption.