Browsing by Author "Yuan, X."
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Item Open Access Influence of stochastic estimation on the control of subsonic cavity flow – A preliminary study(AIAA, 2006-06-30) Debiasi, Marco; Little, J.; Serrani, A.; Yuan, X.; Myatt, James; Samimy, MoThis work aims at understanding how the different elements involved in the feedback loop influence the overall control performance of a subsonic cavity flow based on reducedorder modeling. To this aim we compare preliminary and limited sets of experimental results obtained by modifying some relevant characteristics of the loop. Our results support the findings in the literature that use of quadratic stochastic estimation is preferable to the linear one for real-time update of the model parameters. They also seem to indicate the merit of using more than one time sample of the pressure for performing the real-time update of the model through stochastic estimation. The effect of using two different sets of pressure signals for the stochastic estimation also corroborates previous findings indicating the need for optimizing the number and the placement of the sensors used in the feedback control loop. Finally we observed that the characteristics of the actuator can alter significantly the overall control effect by introducing in the feedback loop additional, undesirable frequency components that are not modeled and hence controlled. A compensator for the actuator is currently being designed that will alleviate this problem thus enabling a clearer understanding of the overall control technique.Item Open Access Retrofit self-optimizing control: a step forward towards real implementation(Institute of Electrical and Electronics Engineers (IEEE), 2017-02-14) Ye, L.; Cao, Yi; Yuan, X.; Song, Z.After 15 year development, it is still hard to find any real application of the self-optimizing control (SOC) strategy, although it can achieve optimal or near optimal operation in industrial processes without repetitive realtime optimization. This is partially because of the misunderstanding that the SOC requires to completely reconfigure the entire control system which is generally unacceptable for most process plants in operation, even though the current one may not be optimal. To alleviate this situation, this paper proposes a retrofit SOC methodology aiming to improve the optimality of operation without change of existing control systems. In the new retrofitted SOC systems, the controlled variables (CVs) selected are kept at constant by adjusting setpoints of existing control loops, which therefore constitutes a two layer control architecture. CVs made from measurement combinations are determined to minimise the global average losses. A subset measurement selection problem for the global SOC is solved though a branch and bound algorithm. The standard testbed Tennessee Eastman (TE) process is studied with the proposed retrofit SOC methodology. The optimality of the new retrofit SOC architecture is validated by comparing two state of art control systems by Ricker and Larsson et al., through steady state analysis as well as dynamic simulations.