This study presents the use of a new chemical reactor network(CRN) model and non-uniform injectors to predict the NOx emission pollutant in gas turbine combustor. The CRN uses information from Computational Fluid Dyna...This study presents the use of a new chemical reactor network(CRN) model and non-uniform injectors to predict the NOx emission pollutant in gas turbine combustor. The CRN uses information from Computational Fluid Dynamics(CFD) combustion analysis with two injectors of CH4-air mixture. The injectors of CH4-air mixture have different lean equivalence ratio, and they control fuel flow to stabilize combustion and adjust combustor's equivalence ratio. Non-uniform injector is applied to improve the burning process of the turbine combustor. The results of the new CRN for NOx prediction in the gas turbine combustor show very good agreement with the experimental data from Korea Electric Power Research Institute.展开更多
In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying t...In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.展开更多
基金supported by Research Program supported by Konkuk University, Korea, 2010
文摘This study presents the use of a new chemical reactor network(CRN) model and non-uniform injectors to predict the NOx emission pollutant in gas turbine combustor. The CRN uses information from Computational Fluid Dynamics(CFD) combustion analysis with two injectors of CH4-air mixture. The injectors of CH4-air mixture have different lean equivalence ratio, and they control fuel flow to stabilize combustion and adjust combustor's equivalence ratio. Non-uniform injector is applied to improve the burning process of the turbine combustor. The results of the new CRN for NOx prediction in the gas turbine combustor show very good agreement with the experimental data from Korea Electric Power Research Institute.
基金supported by the National Natural Science Foundation of China(Nos.6117075,60835004)the National High Technology Research and Development Program of China(863 Program)(Nos.2012AA111004,2012AA112312)
文摘In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.
