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.
Papers published on a yearly basis
Papers
More filters
TL;DR: A novel hybrid estimator consisting of an extended Kalman filter (EKF) and an active power-based model reference adaptive system (AP-MRAS) in order to solve simultaneous estimation problems of the variations in stator resistance and rotor resistance for speed-sensorless induction motor control is presented.
Abstract: This paper presents a novel hybrid estimator consisting of an extended Kalman filter (EKF) and an active power-based model reference adaptive system (AP-MRAS) in order to solve simultaneous estimat...
32 citations
TL;DR: In this paper, a diagnostic method consisting of a combination of Kalman filters and Bayesian belief networks (BBN) is presented, which uses a priori information derived by a BBN at each time step, to derive estimations of the unknown health parameters.
Abstract: A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Network (BBN) is presented. A soft-constrained Kalman filter uses a priori information derived by a BBN at each time step, to derive estimations of the unknown health parameters. The resulting algorithm has improved identification capability in comparison to the stand-alone Kalman filter. The paper focuses on a way of combining the information produced by the BBN with the Kalman filter. An extensive set of fault cases is used to test the method on a typical civil turbofan layout. The effectiveness of the method is thus demonstrated, and its advantages over individual constituent methods are presented.
32 citations
01 Jan 2009
TL;DR: In this article, adaptive fading extended Kalman filter (AFEKF) and adaptive unscented Kalman Filter (ASF) are proposed to reduce the effect of noise variance uncertainty.
Abstract: This paper presents the state estimation problem for nonlinear industrial systems using asynchronous measurements to simulate the circumstances of real case studies. The well-known conventional Kalman filters give the optimal solution but require synchronous measurements, an accurate system model and exact stochastical noise characteristics. Thus, the Kalman filter with incomplete information and asynchronous sensors measurements may be degraded or even diverged. In order to reduce the effect of noise variance uncertainty, adaptive fading extended Kalman filter and adaptive unscented Kalman filter are proposed to overcome this drawback. On the other hand, received data to estimation nodes from multi-sensors have different communication delays and various sampling rates. In this paper, conventional Kalman filter has been modified in a way to be workable for state estimation in plants with different communication delays in their sensors. Also decentralized multi sensor fusion has been used to estimate states in presence of multi-rate sensors. The feasibility and effectiveness of the presented methods are demonstrated through simulation studies on a continuous stirred tank reactor (CSTR) benchmark problem.
31 citations
TL;DR: A decompositional procedure of observer state synthesis with sigmoidal correcting influences is developed in order to get current estimates of the unmeasured state variables and existing uncertainties of the observer in the pre-limit situation.
Abstract: For the synthesis problem for an invariant tracking system for a nonlinear automated control object under incomplete measurements, we develop a decompositional procedure of observer state synthesis with sigmoidal correcting influences in order to get current estimates of the unmeasured state variables and existing uncertainties. This observer in the pre-limit situation possesses the advantages of an observer with discontinuous correcting influences operating in sliding mode; in particular, it lets us estimate external influences without introducing their dynamical model. Unlike a sliding mode observer whose order has been extended due to filters over discontinuous correcting influences, the dimension of this observer equals the dimension of the control object, and in a microprocessor implementation this observer ensures better quality (smoothness) of the signals being estimated.
31 citations
07 Dec 2015
TL;DR: This paper proposes an adaption mechanism for the Kalman filter which is able to filter out shot-noises similarly as has been proposed in context of adaptive and robust Kalman filtering and compares the proposed approach to other approaches using measurement data collected with a smartphone.
Abstract: This paper is concerned with inertial-sensor-based tracking of the gravitation direction in mobile devices such as smartphones. Although this tracking problem is a classical one, choosing a good state-space for this problem is not entirely trivial. Even though for many other orientation related tasks a quaternion-based representation tends to work well, for gravitation tracking their use is not always advisable. In this paper we present a convenient linear quaternion-free state-space model for gravitation tracking. We also discuss the efficient implementation of the Kalman filter and smoother for the model. Furthermore, we propose an adaption mechanism for the Kalman filter which is able to filter out shot-noises similarly as has been proposed in context of adaptive and robust Kalman filtering. We compare the proposed approach to other approaches using measurement data collected with a smartphone.
31 citations