A Fuzzy-Kalman filtering strategy for state estimation
Date
2004-06-14
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ORCID
Type
Degree Level
Masters
Abstract
This thesis considers the combination of Fuzzy logic and Kalman Filtering that have traditionally been considered to be radically different. The former is considered heuristic and the latter statistical. In this thesis a philosophical justification for their combination is presented. Kalman Filtering is revised to enable the incorporation of fuzzy logic in its formulation. This formulation is subsequently referred to as the Revised-Kalman Filter. Heuristic membership functions are then used in the Revised-Kalman Filter to substitute for the system and measurement covariance matrices to form a fuzzy rendition of the Kalman Filter. The Fuzzy Kalman Filter formulation is further revised according to a concept referred to as the “Parallel Distributed Compensation” to allow for further heuristic adjustment of the corrective gain. This formulation is referred to as the Parallel Distributed Compensated-Fuzzy Kalman Filter. Simulated implementations of the above filters reveal that a tuned Kalman Filter provides the best performance. However, if conditions change, the Kalman filter’s performance degrades and a better performance is obtained from the two versions of the Fuzzy Kalman Filters.
Description
Keywords
Control, Identification, Game theory, Estimation, Marriage, Kalman filter, System, Fuzzy, State
Citation
Degree
Master of Science (M.Sc.)
Department
Mechanical Engineering
Program
Mechanical Engineering