Browsing by Author "Hsieh, CS"

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Application of model-matching techniques to feedforward active noise controller design
(1998-01-01)
Hu, JS
;
Yu, SH
;
Hsieh, CS
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, three digital model-matching techniques in H-2, H-infinity, and l(1) performance measures are applied to design digital feedforward controllers for active noise cancellation in ducts. Different measures account for different optimization objectives in terms of physical signals. The distributed nature and high-bandwidth requirements of the control system result in a large set of parameters in plant description and these design techniques proved to be useful insolving the controllers numerically. Experiments were conducted using a floating-point digital signal processor that produced broad-band noise reduction. Design variations and noise reduction effects in terms of human perception are also discussed. It is experimentally proved that using model-matching designs, the causality principle originally raised by Paul Lueg does not have to be satisfied in order to actively reduce the noise level.
Application of model-matching techniques to feedforward active noise controller design
(1998-01-01)
Hu, JS
;
Yu, SH
;
Hsieh, CS
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, three digital model-matching techniques in H-2, H-infinity, and l(1) performance measures are applied to design digital feedforward controllers for active noise cancellation in ducts. Different measures account for different optimization objectives in terms of physical signals. The distributed nature and high-bandwidth requirements of the control system result in a large set of parameters in plant description and these design techniques proved to be useful insolving the controllers numerically. Experiments were conducted using a floating-point digital signal processor that produced broad-band noise reduction. Design variations and noise reduction effects in terms of human perception are also discussed. It is experimentally proved that using model-matching designs, the causality principle originally raised by Paul Lueg does not have to be satisfied in order to actively reduce the noise level.
General two-stage Kalman filters
(2000-04-01)
Hsieh, CS
;
Chen, FC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A general two-stage Kalman filter (GTSKF) that is equivalent to, but numerically more efficient than, the standard single-stage Kalman Biter is developed for general, time-varying, linear discrete-time systems. Analytical results defining the reduction in computational burdens are presented. Simulation results that validate the predicted efficiency improvements are shown as well.
Modified stochastic Luenberger observers
(2000-12-01)
Hsieh, CS
;
Chen, FC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A modified stochastic Luenberger observer (MSLO) structure is proposed to recover the optimal performance of the coventional SLO for obtaining full-state estimates in linear discrete-time stochastic systems. The optimal MSLO (OMSLO) which gives the MMSE estimates is derived by using the general two-stage Kalman filter. A reduced-order form of the OMSLO is also proposed for systems having singular measurement noises. The connection between the OMSLO and the optimal minimal-order observer of Leondes and Novak is also shown. (C) 2000 Elsevier Science Ltd. All rights reserved.
Optimal minimal-order least-squares estimators via the general two-stage Kalman filter
(2001-11-01)
Hsieh, CS
;
Chen, FC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A direct derivation of the optimal minimal-order least-squares estimator (OMOLSE) is presented using the recently developed general two-stage Kalman filter (GTSKF). Using this new result, the reduced-order estimators of O'Reilly and Fairman are readily shown to be equivalent. A practical implementation issue to consider these two estimators is also addressed.
Optimal multistage Kalman estimators
(2000-11-01)
Chen, FC
;
Hsieh, CS
;
電控工程研究所
;
Institute of Electrical and Control Engineering
An optimal multistage Kalman estimator (OMSKE) is proposed as a generalization of the optimal two-stage Kalman estimator for the reduction of the computational burden of the Kalman estimator (KE) for discrete-time linear time-varying systems with triangular transition matrices. This new filter is obtained by applying a multistage U - V transformation to decouple the covariances of the KE. It is shown analytically that the computational complexity of the OMSKE is less than that of the KE and is minimum when the system transition matrix has the maximum stage number.
Optimal solution of the two-stage Kalman estimator
(1995)
Hsieh, CS
;
Chen, FC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Optimal solution of the two-stage Kalman estimator
(1999-01-01)
Hsieh, CS
;
Chen, FC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
The two-stage Kalman estimator was originally proposed to reduce the computational complexity of the augmented state Kalman filter. Recently, it was also applied to the tracking of maneuvering targets by treating the target acceleration as a bias term, Except in certain restrictive conditions, the conventional two-stage estimators are suboptimal in the sense that they are not equivalent to the augmented state filter. In this paper, the authors propose a new two-stage Kalman estimator, i.e. new structure, which is an extension of Friedland's estimator and is optimal in general conditions, In addition, we provide some analytic results to demonstrate the computational advantages of two-stage estimators over augmented ones.