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- 저자 : Nicholas Pooley (검색결과 2 건)
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Mitigating Control Loop Interactions
Ming Tham,Nicholas Pooley,Amin Ganjian,Richard Jones 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
This paper examines the feasibility of using disturbance observers (DOs) to compensate for interactions between control loops in non-linear multivariable systems. A distillation column application is considered. Traditionally, both steady-state and dynamic decouplers, designed using a linear model of the nonlinear system, are used to improve the multi-loop control of distillation columns. The multi-loop control strategy used here is proportional and integral (PI) feedback control on each loop with the controller parameters being derived from the linear model of the column. Steady-state and dynamic decoupling work well on linear systems but less so on nonlinear systems when there is a degree of mismatch between the linear model used to design the decouplers and the underlying non-linear system. Previous work on DOs has indicated their ability to compensate for system nonlinearities, implying that the use of DO’s for decoupling in nonlinear multivariable systems might provide an improvement over the traditional decoupling strategies. Control studies on the non-linear distillation column model indicate that the dynamic decoupler is extremely sensitive to model mismatch, leading to severe deterioration in performance when applied to the non-linear system. The steady-state decoupler was less sensitive to model mismatch and indeed, tends to be the approach used practically. DOs, on the other hand, alleviated the effects of loop interaction quite well even in the presence of modeling errors. This suggests that DOs will work well in a non-linear environment in the presence of model mismatch, perhaps providing much better robust performance than that displayed by the traditional decoupling approaches.
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Genome-wide association study identifies novel breast cancer susceptibility loci
Easton, Douglas F.,Pooley, Karen A.,Dunning, Alison M.,Pharoah, Paul D. P.,Thompson, Deborah,Ballinger, Dennis G.,Struewing, Jeffery P.,Morrison, Jonathan,Field, Helen,Luben, Robert,Wareham, Nicholas Nature Publishing Group 2007 Nature Vol.447 No.7148
Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r<SUP>2</SUP> > 0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P < 10<SUP>-7</SUP>). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P < 0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach.
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