2012
DOI: 10.1109/tie.2011.2161651
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A New Method for Sensorless Estimation of the Speed and Position in Brushed DC Motors Using Support Vector Machines

Abstract: Abstrac/-Currently, for many applications, it is necessary to know the speecl and position oF motors. This can be achievecl nsing mechanical sensors coupled to the motor shaFt or using sensorless technic¡ues. The sensorless technic¡ues in brushed de motors can be classifiecl into two types: 1) technic¡ues basecl 011 the clynamic brushed de motor moclel ancl 2) technic¡ues basecl 011 the ripple componen! oF the curren!. This paper presents a new methocl, based on the ripple component, for speed ancl position es… Show more

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Cited by 52 publications
(23 citation statements)
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“…For example, in [6], Borji et al modeled top-down visual guidance under the influence of different tasks. Multiple factors, such as scene context, physical actions, and bottom-up saliency, are integrated either through popular statistical tools, such as K-NN and support vector machine (SVM) [14], [39], or through a conventional Bayesian network (BN). Borji et al [5] have further designed a new BN to describe task-driven visual attention, where global scene context, previously attended locations and motor actions, are integrated over time to predict the next attended location.…”
Section: Related Workmentioning
confidence: 99%
“…For example, in [6], Borji et al modeled top-down visual guidance under the influence of different tasks. Multiple factors, such as scene context, physical actions, and bottom-up saliency, are integrated either through popular statistical tools, such as K-NN and support vector machine (SVM) [14], [39], or through a conventional Bayesian network (BN). Borji et al [5] have further designed a new BN to describe task-driven visual attention, where global scene context, previously attended locations and motor actions, are integrated over time to predict the next attended location.…”
Section: Related Workmentioning
confidence: 99%
“…, N, where x i denotes the components of the N-dimensional column vector that are the classifier inputs, y i denotes the class labels that are the classifier outputs (and can be 1, −1 for class 1, class −1, respectively), and N is the size of the training set. The linear function [22] that separates both classes is…”
Section: B Support Vector Machinesmentioning
confidence: 99%
“…Sensorless vector control has been applied to brushless DC motors, induction motors, and synchronous motors [2]- [4]. On the other hand, sensorless control methods of DC motors estimating rotor position or velocity from current ripple have been proposed [5]- [9]. However, these methods cannot estimate rotor velocity continuously since they use current ripple for estimation.…”
Section: Introductionmentioning
confidence: 99%