Title :
Multiple-model estimation with variable structure. III. Model-group switching algorithm
Author :
Li, X. Rong ; Xiaorong Zwi ; Youmin Zwang
Author_Institution :
Dept. of Electr. Eng., New Orleans Univ., LA, USA
fDate :
1/1/1999 12:00:00 AM
Abstract :
A general multiple-model (MM) estimator with a variable structure (VSMM), railed model-group switching (MGS) algorithm, is presented. It assumes that the total set of models can be covered by a number of model groups, each representing a cluster of closely related system behavior patterns or structures, and a particular group is running at any given time determined by a hard decision. This algorithm is the first VSMM estimator that is generally applicable to a large class of problems with hybrid (continuous and discrete) uncertainties. It is also easily implementable. It is illustrated, via a simple fault detection and identification example, that the MGS algorithm provides a substantial reduction in computation while having identical performance with the fixed-structure Interacting Multiple-Model (FSIMM) estimator
Keywords :
adaptive estimation; adaptive filters; computational complexity; covariance matrices; directed graphs; linear systems; state estimation; target tracking; tracking filters; Markov chain; adaptive estimation; computation reduction; covariance matrix; digraph switching scheme; fault detection; fault identification; hard decision; hard switching; hybrid uncertainties; jump linear systems; minimum MSE; multiple-model estimation; number of model groups; railed model-group switching algorithm; state estimate; stochastic hybrid system; system modes; target tracking; total set of models; transition probabilities; variable structure; Adaptive estimation; Automatic control; Clustering algorithms; Concurrent computing; Electronic mail; Fault detection; Power engineering and energy; Power engineering computing; Switches; Uncertainty;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
