Affiliation:
1. University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, Canada
Abstract
This paper presents a new strategy for state estimation. The strategy may be applied to linear systems and is referred to as the variable structure filter. The filter is considered for discrete-time systems subject to random disturbances and measurement noise. It requires a parametric model and can be formulated to accommodate modeling uncertainties. A proof of stability for the filter is provided. For stability, this concept requires a specification of an upper bound for uncertainties, disturbances, and measurement noise. The application of this filter to a third-order linear system is demonstrated.
Subject
Computer Science Applications,Mechanical Engineering,Instrumentation,Information Systems,Control and Systems Engineering
Reference25 articles.
1. Kalman, R.E.
, 1960, “A New Approach to Linear Filtering and Prediction Problems,” ASME J. Basic Eng., 82, 35–45.
2. Brown, R.G., and Hwang, P.Y.C., 1997, “Introduction to Random Signals and Applied Kalman Filtering,” Third Edition, Wiley, New York.
3. Horton, M.P.
, 1997, “Real-Time Identification of Missile Aerodynamics using Linearized Kalman Filter Aided by an Artificial Neural Network,” IEE Proc.: Control Theory Appl., 144.
4. Sorenson, H.W.
, 1970, “Least-Squares Estimation: From Gauss to Kalman,” IEEE Spectrum, 7, pp. 63–68.
5. Grewal, M.S., and Andews, A.P., 1993, “Kalman Filtering Theory and Practice,” Prentice-Hall, Englewood Cliffs, NJ.
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