Topic
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.
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TL;DR: In this article, differential flatness properties and an input-output linearization procedure for doubly fed induction generators (DFIGs) are studied. And a comparison of the differentialflatness theory-based control method against Lie algebra-based controller is provided.
Abstract: The paper studies differential flatness properties and an input–output linearization procedure for doubly fed induction generators (DFIGs) By defining flat outputs which are associated with the rotor's turn angle and the magnetic flux of the stator, an equivalent DFIG description in the Brunovksy (canonical) form is obtained For the linearized canonical model of the generator, a feedback controller is designed Moreover, a comparison of the differential flatness theory-based control method against Lie algebra-based control is provided At the second stage, a novel Kalman Filtering method (Derivative-free nonlinear Kalman Filtering) is introduced The proposed Kalman Filter is redesigned as disturbance observer for estimating additive input disturbances to the DFIG model These estimated disturbance terms are finally used by a feedback controller that enables the generator's state variables to track desirable setpoints The efficiency of the proposed state estimation-based control scheme is tested through simulation experiments
30 citations
TL;DR: In this article, it was shown that the RKF achieves zero steady-state variance of the estimation error if and only if the plant has no transmission zeros in the right-half plane, since these would be among the poles of the Kalman filter.
Abstract: Several known results are unified by considering properties of reduced-order Kalman filters. For the case in which the number of noise sources equals the number of observations, it is shown that the reduced-order Kalman filter achieves zero steady-state variance of the estimation error if and only if the plant has no transmission zeros in the right-half plane, since these would be among the poles of the Kalman filter. The reduced-order Kalman filter cannot achieve zero variance of the estimation error if the number of independent noise sources exceeds the number of observations. It is also shown that the reduced-order Kalman filter achieves the generalized Doyle-Stein condition for robustness when the noise sources are colocated with the control inputs. When there are more observations than noise sources, additional noise sources can be postulated to improve the observer frequency response without diminishing robustness. >
30 citations
TL;DR: It is shown that, for a scalar random walk system in which the two noise sources have equal variance, the Kalman filter's estimate turns out to be a convex linear combination of the a priori estimate and of the measurements with coefficients suitably related to the Fibonacci numbers.
30 citations
TL;DR: In this paper, the normalised innovation squared NIS test is used to assess whether a Kalman filter's noise assumptions are consistent with realised measurements, which can be applied online with real data, and does not require future data, repeated experiments or knowledge of the true state.
Abstract: The normalised innovation squared NIS test, which is used to assess whether a Kalman filter's noise assumptions are consistent with realised measurements, can be applied online with real data, and does not require future data, repeated experiments or knowledge of the true state. In this work, it is shown that the NIS test is equivalent to three other model criticism procedures, which are as follows: i it can be derived as a Bayesian p-test for the prior predictive distribution; ii as a nested-model parameter significance test; and iii from a recently-proposed filter residual test. A new NIS-like test corresponding to a posterior predictive Bayesian p-test is presented. Copyright © 2015John Wiley & Sons, Ltd.
30 citations
TL;DR: This technical note presents a new Receding-horizon Nonlinear Kalman (RNK) filter for state estimation in nonlinear systems with state constraints, which results in a quadratic program (QP) problem for the corrector step when the measurement model is linear, irrespective of the state propagation model.
Abstract: This technical note presents a new Receding-horizon Nonlinear Kalman (RNK) filter for state estimation in nonlinear systems with state constraints. Such problems appear in almost all engineering disciplines. Unlike the Moving Horizon Estimation (MHE) approach, the RNK Filter formulation follows the Kalman Filter (KF) predictor-corrector framework. The corrector step is solved as an optimization problem that handles constraints effectively. The performance improvement and robustness of the proposed estimator vis-a-vis the extended Kalman filter (EKF) are demonstrated through nonlinear examples. These examples also demonstrate the computational advantages of the proposed approach over the MHE formulation. The computational gain is due to the fact that the proposed RNK formulation avoids the repeated integration within an optimization loop that is required in an MHE formulation. Further, the proposed formulation results in a quadratic program (QP) problem for the corrector step when the measurement model is linear, irrespective of the state propagation model. In contrast, a nonlinear programming problem (NLP) needs to be solved when an MHE formulation is used for such problems. Also, the proposed filter for unconstrained linear systems results in a KF estimate for the current instant and smoothed estimates for the other instants of the receding horizon.
30 citations