Abstract
For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system.
| Language | English |
|---|---|
| Pages | 1785-1804 |
| Number of pages | 20 |
| Journal | International Journal of Systems Science |
| Volume | 43 |
| Issue number | 10 |
| Early online date | 28 Jul 2011 |
| DOIs | |
| Publication status | Published - 1 Oct 2012 |
| Externally published | Yes |
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Optimised configuration of sensors for fault tolerant control of an electro-magnetic suspension system. / Michail, K.; Zolotas, A. C.; Goodall, R. M.; Whidborne, J. F.
In: International Journal of Systems Science, Vol. 43, No. 10, 01.10.2012, p. 1785-1804.Research output: Contribution to journal › Article
TY - JOUR
T1 - Optimised configuration of sensors for fault tolerant control of an electro-magnetic suspension system
AU - Michail, K.
AU - Zolotas, A. C.
AU - Goodall, R. M.
AU - Whidborne, J. F.
PY - 2012/10/1
Y1 - 2012/10/1
N2 - For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system.
AB - For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system.
KW - Electromagnetic suspension
KW - Genetic algorithms
KW - LQG control
KW - Optimised sensor configurations
KW - Sensor fault tolerance
UR - http://www.scopus.com/inward/record.url?scp=84865232039&partnerID=8YFLogxK
U2 - 10.1080/00207721.2011.598959
DO - 10.1080/00207721.2011.598959
M3 - Article
VL - 43
SP - 1785
EP - 1804
JO - International Journal of Systems Science
T2 - International Journal of Systems Science
JF - International Journal of Systems Science
SN - 0020-7721
IS - 10
ER -