A hierarchical multiple model adaptive control of discrete-time stochastic systems for sensor and actuator uncertainties

Abstract

A hierarchical multiple model adaptive control (MMAC) is described for discrete-time stochastic systems with unknown sensor and actuator parameters, where the decentralized structure consists of a central processor and of m local processors which do not communicate between each other. A major assumption in this study is that the central and any local stations have different knowledge of the hypotheses on the unknown parameters. This leads to a flexible design algorithm for passively adaptive control strategies. Furthermore, the coordinator algorithm in evaluating the global a posteriori probability is relatively simple to implement. The result is applied to the design problem of an instrument failure detection and identification (FDI) system.