Multiple-model estimation with variable structure: model-group switching algorithm

• Published in: Proceedings of the 36th IEEE Conference on Decision and Control Vol. 4; pp. 3114 - 3119 vol.4
• Main Authors: Li, X.P., Youmin Zhang, Xiaorong Zhi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 1997
• Subjects: Algorithm design and analysis; Clustering algorithms; History; Noise measurement; State estimation; Stochastic systems; Target tracking; Time measurement; Uncertainty; Vectors
• ISBN: 0780341872; 9780780341876
• ISSN: 0191-2216
• DOI: 10.1109/CDC.1997.652320

A general multiple-model estimator with variable structure (VSMM), called model-group switching 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 problem with hybrid (continuous and discrete) uncertainties and easily implementable. The algorithm is promising in the sense of being substantially more cost-effective than the interacting multiple-model estimator.