• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.4
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• pp.68-75
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• 2012

This paper presents a study on the driveline output torque estimation using a discrete Kalman filter. The in-situ output shaft torque is first measured by a non-contacting magneto-elastic torque transducer. The linear state-space system equations are first derived and the discrete Kalman filter is designed based on the Kalman filter theory to recover the driveline output torque contaminated by random noises. In addition to using torque measurement, the estimation of the output torque using two angular velocities: the output and wheel, is also conducted. The experimental results show that the discrete Kalman filter can be effective for not only removing the random noise in output torque but also estimating the output torque without torque measurement.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.7
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• pp.444-451
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• 2004

Conventional magnetic levitation systems could show unsatisfactory performance under air-gap disturbance due to rail irregularities. In this paper, we propose a feedback control system with discrete Kalman filter for air-gap disturbance attenuation. It is shown that excellent system performance can be obtained with the use of discrete Kalman filter, and that results from experiments agree well with those of simulations.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.351-353
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• 2006

In this paper, we present the traditional GPS Position- Velocity (PV) model to apply for both Discrete-Time Kalman Filter and Discrete-Time $H_{\infty}$ Filter. The positioning algorithms of both filters are proposed for a stand-alone low-cost GPS module to increase its accuracy. For disturbance cancellation, the Kalman Filter requires the statistical information about process and measurement noises while the $H_{\infty}$ Filter only requires that these noises are bounded. Experiments show that with the same measurement data, $H_{\infty}$ Filter gives us better positioning results compared with Least-Squared method and Kalman Filter.

• Proceedings of the KIEE Conference
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• 2004.04a
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• pp.253-255
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• 2004

Conventional magnetic levitation systems could show unsatisfactory performance under air-gap disturbance due to rail irregularities. In this paper, we propose a feedback control system with discrete Kalman filter for air-gap disturbance attenuation. It is shown that excellent system performance can be obtained with the use of discrete Kalman filter, and that results from experiments agree well with those of simulations.

• Journal of Industrial Technology
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• v.10
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• pp.15-20
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• 1990

A parallel processing algorithm for discrete Kalman filter, which is one of the most commonly used filtering technique in modern control, signal processing, and communication, is proposed. Previously proposed parallel algorithms to decrease the number of computations needed in the Kalman filter are the hierachical structures by distributed processing of measurements, or the systolic structures to disperse the computational burden. In this paper, a new parallel Kalman filter employing a structure similar to recursive doubling is proposed. Estimated values of state variables by the new algorithm converge with two times faster data processing speed than that of the conventional Kalman filter. Moreover it maintains the optimality of the conventional Kalman filter.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.10
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• pp.1057-1065
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• 1990

A parallel processing algorithm for the discrete Kalman filter, which is one of the most commonly used filtering techniques in modern control, signal processing, and communication, is proposed. To decrease the number of computations critical in the Kalman filter, previously proposed parallel algorithms are of the hierarchical structure by distributed processing of measurements, or of the systolic structure to disperse the computational burden. In this paper, a new parallel Kalman filter employing a structure similar to recursive doubling is proposed. Estimated valuse of state variables by the new algorithm converge faster to the true values because the new algorithm can process data twice faster than the conventional Kalman filter. Moreover, it maintains the optimality of the conventional Kalman filter.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.64-67
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• 1990

A parallel processing algorithm for discrete Kalman filter, which is one of the most commonly used filtering technique in modern control, signal processing, and communication. is proposed. Previously proposed parallel algorithms to decrease the number of computations needed in the Kalman filter are the hierachical structures by distributed processing of measurements, or the systolic structures to disperse the computational burden. In this paper, a new parallel Kalman filter employing a structure similar to recursive doubling is proposed. Estimated values of state variables by the new algorithm converge with two times faster data processing speed than that of the conventional Kalman filter. Moreover it maintains the optimality of the conventional Kalman filter.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.363-363
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• 2001

We study on the velocity matching algorithm for transfer alignment of inertial navigation system(INS) using a robust H₂ filter. We suggest an uncertainty model and a discrete robust H₂filter for INS and apply the suggested robust H₂ filter to the uncertainty model. The discrete robust H₂filter is shown by simulation to have better performance time and accuracy than Kalman filter.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.201-207
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• 2006

A robust Kalman filtering method for uncertain stochastic systems is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state transition equation. The predictor and corrector of discrete Kalman filter are reformulated with the perturbation estimator. Successively, the state and perturbation estimation error dynamics and the corresponding error covariance propagation equations are derived as well. Finally we have the recursive algorithm of Combined Kalman Filter-Perturbation Estimator (CKF). The proposed combined Kalman filter-perturbation estimator has the property of integrating innovations and the adaptation capability to system uncertainties. A numerical example is shown to demonstrate the effectiveness of the proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.26-26
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• 2000

We study on the velocity matching algorithm for transfer alignment of inertial navigation system(INS) using robust H$_2$ filter. We suggest an uncertainty model for INS and apply the suggested discrete robust H$_2$ filter to the uncertainty model compared with kalman filter, the discrete robust H$_2$ filter is shown by simulation to have good performance of alignment time and accuracy.