Stochastic Control for Systems With Faulty Sensors

[+] Author and Article Information
K. Watanabe

College of Engineering, Shizuoka University, Johoku 3-5-1 Hamamatsu 432, Japan

S. G. Tzafestas

Division of Computer Science, Department of Electrical Engng., National Technical Univ. of Athens, Zografou 157 73, Athens, Greece

J. Dyn. Sys., Meas., Control 112(1), 143-147 (Mar 01, 1990) (5 pages) doi:10.1115/1.2894131 History: Received April 29, 1988; Revised September 10, 1988; Online March 17, 2008


The problem of control of linear discrete-time stochastic systems with faulty sensors is considered. The anomaly sensors are assumed to be modeled by a finite-state Markov chain whose transition probabilities are completely known. A passive type multiple model adaptive control (MMAC) law is developed by applying a new generalized pseudo-Bayes algorithm (GPBA), which is based on an n-step measurement update method. The present and other existing algorithms are compared through some Monte Carlo simulations. It is then shown that, for a case of only measurement noise uncertainty (i.e., a case when the certainty equivalence principle holds), the proposed MMAC has better control performance than MMAC’s based on using other existing GPBA’s.

Copyright © 1990 by The American Society of Mechanical Engineers
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