A Smooth Variable Structure Position Control Of Dc Motor

Bandyopadhyay, B.; Paul, Arun Kumar; Srisailam, M.C.

doi:10.1109/tencon.1991.712509

Cited by 7 publications

(1 citation statement)

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“…The angular position control of these motors is an important challenge and requires accuracy. Different controllers for angular position have been proposed, for example neural networks (NN) ( [2], [30]), the classical proportional integral derivative (PID) [28], sliding mode ( [1], [3], [7]), fuzzy-sliding mode [31], PID combined with fuzzy sliding mode [9] controllers, among others.…”

Section: Introductionmentioning

confidence: 99%

Direct current motor control based on high order neural networks using stochastic estimation

Castaneda

Esquivel

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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An adaptive discrete-time tracking controller for a direct current (DC) motor with controlled excitation flux is presented. A high order neural network in discrete-time is used to identify the plant model; this network is trained with an extended Kalman filter where the associated state and measurement noises discrete-time covariance matrices are calculated with stochastic estimation. Then, the discrete-time block control and sliding mode techniques are used to develop the trajectory tracking for the angular position of a DC motor with separate winding excitation. Numerical computation presented in this paper shows that the proposed method provides accurate estimation for the covariance matrices associated in the extended Kalman filter.

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