Abstract
A method of hierarchical multiple model adaptive estimation and control is proposed for discrete-time distributed systems with unknown parameters, in which the decentralized structure consists of a central station and of two local stations which do not communicate between each other. It is assumed that the global and local hypotheses on the unknown parameters are introduced to the decentralized multiple model structure. This modeling provides a flexible design algorithm for passively adaptive control problems in steady-state. Instead of utilizing the local a posteriori probabilities, the probability density functions of the innovation processes for the local Kalman filters are adopted to simply reconstruct the global a posteriori probability in the central station. The effectiveness of the present method is illustrated through a numerical simulation of the FDI (failure detection and idetification) system for a hydrofoil boat.
