Stochastic control for systems with faulty sensors

Keigo Watanabe, S. G. Tzafestas

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)143-147
Number of pages5
JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Volume112
Issue number1
Publication statusPublished - Mar 1990
Externally publishedYes

Fingerprint

adaptive control
Markov chains
sensors
Sensors
noise measurement
transition probabilities
equivalence
anomalies
Stochastic systems
Markov processes
simulation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Instrumentation

Cite this

Stochastic control for systems with faulty sensors. / Watanabe, Keigo; Tzafestas, S. G.

In: Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, Vol. 112, No. 1, 03.1990, p. 143-147.

Research output: Contribution to journalArticle

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