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

 
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1990 (EN)
A hierarchical multiple model adaptive control of discrete-time stochastic systems for sensor and actuator uncertainties (EN)

Watanabe, K (EN)
Tzafestas, SG (EN)

N/A (EN)

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. (EN)

journalArticle

Computer Programming--Algorithms (EN)
Actuators (EN)
Parameter estimation (EN)
Hierarchical decision making (EN)
Probability (EN)
Control Systems, Stochastic (EN)
Decentralized Control (EN)
Control Systems, Discrete Time (EN)
Decentralized control (EN)
Failure detection (EN)
Kalman filters (EN)
Control Systems, Adaptive (EN)
Failure Detection and Identification (EN)

Εθνικό Μετσόβιο Πολυτεχνείο (EL)
National Technical University of Athens (EN)

Automatica (EN)

1990


PERGAMON-ELSEVIER SCIENCE LTD (EN)



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