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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Papers
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TL;DR: In this article, a novel adaptive filtering technique is described for a class of systems with unknown disturbances, which includes both a self-tuning filter and a Kalman filter, and state estimates are employed in a closed-loop feedback control scheme which is designed via the usual linear quadratic approach.
Abstract: A novel adaptive filtering technique is described for a class of systems with unknown disturbances. The estimator includes both a self-tuning filter and a Kalman filter. The state estimates are employed in a closed-loop feedback control scheme which is designed via the usual linear quadratic approach. The approach was developed for application to the dynamic ship positioning control problem and has the advantage that existing nonadaptive Kalman filtering systems may be easily modified to include the self-tuning feature.
144 citations
01 Jan 1996
TL;DR: The Kalman filter is used as a basis for parameter stability testing for Flexible Least Squares, and parameter estimation for Parameter estimation is carried out with similar results.
Abstract: Preface. 1. Introduction. 2. Test for parameter stability. 3. Flexible Least Squares. 4. The Kalman filter. 5. Parameter estimation. 6. The estimates, reconsidered. 7. Modeling with the Kalman filter. A. Tables of references. B. The programs and the data. Bibliography. Index.
144 citations
TL;DR: Numerically the optimal fast tracking observer bandwidth and the absolute tracking error estimation for a class of non-linear and uncertain motion control problems by finite difference method are studied.
Abstract: In current industrial control applications, the proportional + integral + derivative (PID) control is still used as the leading tool, but constructing controller requires precise mathematical model of plant, and tuning the parameters of controllers is not simple to implement. Motivated by the gap between theory and practice in control problems, linear active disturbance rejection control (LADRC) addresses a set of control problems in the absence of precise mathematical models. LADRC has two parameters to be tuned, namely, a closed-loop bandwidth and observer bandwidth. The performance of LADRC depends on the quick convergence of a unique state observer, known as the extended state observer, proposed by Jinqing Han (1994). Only one parameter, observer bandwidth, significantly affects the tracking speed of extended state observer. This paper studies numerically the optimal fast tracking observer bandwidth and the absolute tracking error estimation for a class of non-linear and uncertain motion control probl...
143 citations
10 Dec 2007
TL;DR: A modified Kalman filter is introduced that can perform robust, real-time outlier detection in the observations, without the need for manual parameter tuning by the user, using a weighted least squares-like approach.
Abstract: In this paper, we introduce a modified Kalman filter that can perform robust, real-time outlier detection in the observations, without the need for manual parameter tuning by the user. Robotic systems that rely on high quality sensory data can be sensitive to data containing outliers. Since the standard Kalman filter is not robust to outliers, other variations of the Kalman filter have been proposed to overcome this issue, but these methods may require manual parameter tuning, use of heuristics or complicated parameter estimation. Our Kalman filter uses a weighted least squares-like approach by introducing weights for each data sample. A data sample with a smaller weight has a weaker contribution when estimating the current time step's state. We learn the weights and system dynamics using a variational Expectation-Maximization framework. We evaluate our Kalman filter algorithm on data from a robotic dog.
143 citations
TL;DR: In this paper, an extended (nonlinear) Kalman filter is designed to estimate the rapidly varying handling state vector, which is augmented to include adaptive states (cornering stiffnesses) to compensate for tyre force nonlinearities.
Abstract: This paper considers a method for estimating vehicle handling dynamic states in real-time, using a reduced sensor set; the information is essential for vehicle handling stability control and is also valuable in chassis design evaluation. An extended (nonlinear) Kalman filter is designed to estimate the rapidly varying handling state vector. This employs a low order (4 DOF) handling model which is augmented to include adaptive states (cornering stiffnesses) to compensate for tyre force nonlinearities. The adaptation is driven by steer-induced variations in the longitudinal vehicle acceleration. The observer is compared with an equivalent linear, model-invariant Kalman filter. Both filters are designed and tested against data from a high order source model which simulates six degrees of freedom for the vehicle body, and employs a combined-slip Pacejka tyre model. A performance comparison is presented, which shows promising results for the extended filter, given a sensor set comprising three accelerometers only. The study also presents an insight into the effect of correlated error sources in this application, and it concludes with a discussion of the new observer's practical viability.
142 citations