Engineering topics

Verhaegen, M.

Publications and source records attributed to Verhaegen, M..

4 records

NASA NTRS1987

On the reliable and flexible solution of practical subset regression problems

A new algorithm for solving subset regression problems is described. The algorithm performs a QR decomposition with a new column-pivoting strategy, which permits subset selection directly from the originally defined regression parameters. This, in combination with a number of extensions of the new technique, makes the method a very flexible tool for analyzing subset regression problems in which the parameters have a physical meaning.

Verhaegen, M.↗

NASA NTRS1986

Numerical aspects of different Kalman filter implementations

A theoretical analysis is made of the error propagation due to numerical roundoff for four different Kalman filter implementations: the conventional Kalman filter, the square root covariance filter, the square root information filter, and the Chandrasekhar square root filter. An experimental analysis is performed to validate the new insights gained by the theoretical analysis.

Verhaegen, M.↗

NASA NTRS1986

A theoretical analysis of the round-off propagation in different Kalman filter implementations

A theoretical analysis is made of the error propagation due to numerical round-off for four different Kalman filter implementations: the conventional Kalman filter, the square root covariance filter, the square root information filter and the Chandrasekhar square root filter. From these error models, new insights about the applicability of the different filters and their sensitivity to round-off, is obtained. It is shown that the CKF may become completely unreliable when the original plant is unstable, and that this is easily circumvented by a number of techniques. The square root filters, often quoted to possess a conditioning or sensitivity that is the square root of that of the CKF, are shown to possess this property only for the computation of the covariance of the filtered signal and not for the computation of the Kalman gain or the filtered estimate. Finally, the Chandrasekhar filter is shown to be numerically unstable.

Verhaegen, M.↗

NASA NTRS1986

A class of fast staircase algorithms for generalized state-space systems

Several methods are presented for defining generalized state space models (GSSM), with emphasis on 'fast' techniques for transforming GSSMs to condensed state space models (SSM). The fast forms are configured to yield invariant transfer functions. Details of the decomposition process are summarized in terms of the determination of eigenvalues which are separated within staircase matrices. Applications of the techniques are illustrated through implementation of a unimodular transformation which does not affect the finite eigenvalues, by demonstrating deadbeat control of a GSSM, which the definition of a reduced observer of a GSSM, and by embedding a polynomial matrix into a unimodular matrix.

Beelen, T.↗