Pd/γ-Al2O3–TiO2catalysts containing various compositions of titania and alumina were prepared by sol–gel and wet-impregnation methods in attempt to study the particle size, nature of phases, morphology and structur...Pd/γ-Al2O3–TiO2catalysts containing various compositions of titania and alumina were prepared by sol–gel and wet-impregnation methods in attempt to study the particle size, nature of phases, morphology and structure of the composite samples. The ethanol oxidation experiments, N2adsorption–desorption,FTIR, XRD and XPS were conducted, and the effects of Al2O3content on the surface area, phase transformation and structural properties of TiO2were investigated. The optimal value of ethanol conversion appeared on Pd/Al(0.05)–Ti and Pd/Al(0.90)–Ti catalysts irrespective of the ethanol oxidation temperature, and we call this as a double peaks phenomenon of catalytic activity. The XRD results reveal that the phase composition and crystallite size of the mixed oxides depend on Al2O3/TiO2ratio and calcination temperature. Al2O3can effectively prevent the agglomeration of TiO2and this can be ascribed to the formation of Al–O–Ti chemical bonds in Al2O3–TiO2crystals. Binding energy and Pd surface concentration of the catalysts were modified apparently, which may also lead to catalyst activity changes.展开更多
In this paper, we investigate the learning issue in the adaptive neural network （NN） output feedback control of nonlinear systems in Brunovsky canonical form with unknown affine term. With only output measurements, ...In this paper, we investigate the learning issue in the adaptive neural network （NN） output feedback control of nonlinear systems in Brunovsky canonical form with unknown affine term. With only output measurements, a high-gain observer （HGO） is employed to estimate the derivatives of the system output which may be associated with the generation of peaking phenomenon. The adverse effect of peaking on learning and its elimination strategies are analyzed. When the gain of HGO is chosen too high, it may cause the failure of learning from the unknown closed-loop system dynamics. Hence, the gain of HGO is not chosen too high to relieve peaking and guarantee the accuracy of the estimated system states. Then, learning from the unknown closed-loop system dynamics can be achieved. When repeating the same or similar control tasks, a neural learning controller is presented which can effectively recall and reuse the learned knowledge to guarantee the output tracking performance. Finally, simulation results demonstrate the effectiveness of the proposed scheme.展开更多
基金supported by Shanxi Provincial Science and Technology Projects(No.20140313002-2)the National Natural Science Foundation of China(No.21073131)
文摘Pd/γ-Al2O3–TiO2catalysts containing various compositions of titania and alumina were prepared by sol–gel and wet-impregnation methods in attempt to study the particle size, nature of phases, morphology and structure of the composite samples. The ethanol oxidation experiments, N2adsorption–desorption,FTIR, XRD and XPS were conducted, and the effects of Al2O3content on the surface area, phase transformation and structural properties of TiO2were investigated. The optimal value of ethanol conversion appeared on Pd/Al(0.05)–Ti and Pd/Al(0.90)–Ti catalysts irrespective of the ethanol oxidation temperature, and we call this as a double peaks phenomenon of catalytic activity. The XRD results reveal that the phase composition and crystallite size of the mixed oxides depend on Al2O3/TiO2ratio and calcination temperature. Al2O3can effectively prevent the agglomeration of TiO2and this can be ascribed to the formation of Al–O–Ti chemical bonds in Al2O3–TiO2crystals. Binding energy and Pd surface concentration of the catalysts were modified apparently, which may also lead to catalyst activity changes.
文摘In this paper, we investigate the learning issue in the adaptive neural network （NN） output feedback control of nonlinear systems in Brunovsky canonical form with unknown affine term. With only output measurements, a high-gain observer （HGO） is employed to estimate the derivatives of the system output which may be associated with the generation of peaking phenomenon. The adverse effect of peaking on learning and its elimination strategies are analyzed. When the gain of HGO is chosen too high, it may cause the failure of learning from the unknown closed-loop system dynamics. Hence, the gain of HGO is not chosen too high to relieve peaking and guarantee the accuracy of the estimated system states. Then, learning from the unknown closed-loop system dynamics can be achieved. When repeating the same or similar control tasks, a neural learning controller is presented which can effectively recall and reuse the learned knowledge to guarantee the output tracking performance. Finally, simulation results demonstrate the effectiveness of the proposed scheme.
