Alpha beta filter

About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.

Powerful nonlinear observer associated with field-oriented control of an induction motor

Abstract: In this paper, we associate field-oriented control with a powerful nonlinear robust flux observer for an induction motor to show the improvement made by this observer compared with the open-loop and classical estimator used in this type of control We implement this design strategy through an extension of a special class of nonlinear multivariable systems satisfying some regularity assumptions We show by an extensive study that this observer is completely satisfactory at low and nominal speeds and it is not sensitive to disturbances and parametric errors It is robust to changes in load torque, rotational speed and rotor resistance The method achieves a good performance with only one easier gain tuning obtained from an algebraic Lyapunov equation Finally, we present results and simulations with concluding remarks on the advantages and perspectives for the observer proposed with the field-oriented control

Multi-Area Dynamic State Estimation With PMU Measurements by an Equality Constrained Extended Kalman Filter

Abstract: To achieve higher accuracy of estimated dynamic states, phasor measurement unit (PMU) measurements of buses in a network can be used for dynamic state estimation (DSE). However, it is difficult to coordinate the states of boundary buses in different areas when performing multiarea DSE. By using PMU measurements of buses in the network, this paper proposes an extended Kalman filter-based method with equality constraints to conduct multi-area DSE. First, a corrective strategy is proposed to estimate a corrective internal voltage (CIV) and a corrective rotor angle (CRA) of each generator with all PMUs’ measurements. The proposed corrective strategy can also ensure the consistency of the boundary buses in the multiareas. Then, the CIV and the CRA are used to establish equality constraints for updating the dynamic states with the PMU measurements. An IEEE 30-bus system and an IEEE 118-bus system are used to validate the proposed method, with the results showing the feasibility and accuracy of the proposed method.

A neural extended Kalman filter multiple model tracker

Abstract: A neural extended Kalman filter algorithm was embedded in an interacting multiple model architecture for target tracking. The neural extended Kalman filter algorithm is used to improve motion model prediction during maneuvers. With a better target motion mode, noise reduction can be achieved through a maneuver. Unlike the interacting multiple model architecture which, uses a high process noise model to hold a target through a maneuver with poor velocity and acceleration estimates, a neural extended Kalman filter is used to predict the correct velocity and acceleration states of a target through a maneuver. The neural extended Kalman filter estimates the weights of a neural network, which in turn is used to modify the state estimate predictions of the filter as measurements are processed. The neural network training is performed on-line as data is processed. In this paper, the results of a neural extended Kalman filter embedded in an interacting multiple model tracking architecture will be shown using a high fidelity model of a phased array radar. Six different targets of varying maneuverability will be tracked. The phased array radar is controlled via Level 4 Data Fusion feedback to the Level 0 radar process. Highly maneuvering threats are a major concern for the Navy and DoD and this technology will help address this issue.

Target tracking algorithms for phased array radar

Abstract: Phased array radar have an additional degree of freedom compared with track-while-scan radar, in that a variable update time may be used in the former. On detection of a manoeuvre, the update time may be reduced leading to an improved tracking accuracy. It has been shown how a variable update time may be incorporated into the alpha beta filter, the value of this update time being dependent on the magnitude of the residual. The algorithm of Cohen (1986) is tested by application of Monte-Carlo simulations to a wide variety of target tracks. Additional strategies in choosing the update time are also examined. The dependence of the mean square estimation errors and the mean update times are investigated for a range of parameters of the algorithm. It is concluded that in choosing the parameters of the algorithm, there is a trade-off between keeping the estimation errors low and restricting the mean update time from becoming excessively small. >