Abstract: Rotor Flux based Model Reference Adaptive System
(RF-MRAS) is the most popularly used conventional speed
estimation scheme for sensor-less IM drives. In this scheme, the
voltage model equations are used for the reference model. This
encounters major drawbacks at low frequencies/speed which leads to
the poor performance of RF-MRAS. Replacing the reference model
using Neural Network (NN) based flux estimator provides an
alternate solution and addresses such drawbacks. This paper
identifies an NN based flux estimator using Single Neuron Cascaded
(SNC) Architecture. The proposed SNC-NN model replaces the
conventional voltage model in RF-MRAS to form a novel MRAS
scheme named as SNC-NN-MRAS. Through simulation the proposed
SNC-NN-MRAS is shown to be promising in terms of all major
issues and robustness to parameter variation. The suitability of the
proposed SNC-NN-MRAS based speed estimator and its advantages
over RF-MRAS for sensor-less induction motor drives is
comprehensively presented through extensive simulations.
Abstract: this paper gives a novel approach towards real-time speed estimation of multiple traffic vehicles using fuzzy logic and image processing techniques with proper arrangement of camera parameters. The described algorithm consists of several important steps. First, the background is estimated by computing median over time window of specific frames. Second, the foreground is extracted using fuzzy similarity approach (FSA) between estimated background pixels and the current frame pixels containing foreground and background. Third, the traffic lanes are divided into two parts for both direction vehicles for parallel processing. Finally, the speeds of vehicles are estimated by Maximum a Posterior Probability (MAP) estimator. True ground speed is determined by utilizing infrared sensors for three different vehicles and the results are compared to the proposed algorithm with an accuracy of ± 0.74 kmph.