Research of airport＇ s influence on regional economy is conducted based on system dynamics, combining with input- output method and multiplier effect. SD model was used for visualizing, modeling and simulating airpor...Research of airport＇ s influence on regional economy is conducted based on system dynamics, combining with input- output method and multiplier effect. SD model was used for visualizing, modeling and simulating airport-regional economy system behavior. Model validation using the example of the Xi＇ an airport proved model simulated system behavior accurately. Besides, the result of scenario development simulation provided a new perspective for the practice of airport management and policy-making.展开更多
In this paper, we consider the discrete-time dynamic models of input-output whose input-coefficient matrices A. and capital-coefficient matrices Bn can vary with time n. Under certain conditions, we prove that there e...In this paper, we consider the discrete-time dynamic models of input-output whose input-coefficient matrices A. and capital-coefficient matrices Bn can vary with time n. Under certain conditions, we prove that there exists a subspace of nonnegative vectors Ωr such that if initial (input) product , then there is some natural number n0≥0 such that for n≥ n0, the n-th year’sproduct Xn has at least one negative component, which means that economic dislocation occurs.展开更多
A modeling method is proposed for a dynamic fast steering mirror(FSM) system with dual inputs and dual outputs. A physical model of the FSM system is derived based on first principles, describing the dynamics and coup...A modeling method is proposed for a dynamic fast steering mirror(FSM) system with dual inputs and dual outputs. A physical model of the FSM system is derived based on first principles, describing the dynamics and coupling between the inputs and outputs of the FSM system. The physical model is then represented in a state-space form. Unknown parameters in the state-space model are identified by the subspace identification algorithm, based on the measured input-output data of the FSM system. The accuracy of the state-space model is evaluated by comparing the model estimates with measurements. The variance-accounted-for value of the state-space model is better than 97%, not only for the modeling data but also for the validation data set, indicating high accuracy of the model. Comparison is also made between the proposed dynamic model and the conventional static model, where improvement in model accuracy is clearly observed. The model identified by the proposed method can be used for optimal controller design for closed-loop FSM systems. The modeling method is also applicable to FSM systems with similar structures.展开更多
In this work, several procedures for the fault detection and isolation （FDI） on general aviation aircraft sensors are presented. In order to provide a comprehensive wide-pectrum treatment, both linear and nonlinear,...In this work, several procedures for the fault detection and isolation （FDI） on general aviation aircraft sensors are presented. In order to provide a comprehensive wide-pectrum treatment, both linear and nonlinear, model-based and data-driven methodologies are considered. The main contributions of the paper are related to the development of both FDI polynomial method （PM） and FDI scheme based on the nonLinear geometric approach （NLGA）. As to the PM, the obtained results highlight a good trade-off between solution complexity and resulting performances. Moreover, the proposed PM is especially useful when robust solutions are required for minimising the effects of modelling errors and noise, while maximising fault sensitivity. As to the NLGA, the proposed work is the first development and robust application of the NLGA to an aircraft model in flight conditions characterised by tight-oupled longitudinal and lateral dynamics. In order to verify the robustness of the residual generators related to the previous FDI techniques, the simulation results adopt a typical aircraft reference trajectory embedding several steady-tate flight conditions, such as straight flight phases and coordinated turns. Moreover, the simulations are performed in the presence of both measurement and modelling errors. Finally, extensive simulations are used for assessing the overall capabilities of the developed FDI schemes and a comparison with neural networks （NN） and unknown input Kalman filter （UIKF） diagnosis methods is performed.展开更多
文摘Research of airport＇ s influence on regional economy is conducted based on system dynamics, combining with input- output method and multiplier effect. SD model was used for visualizing, modeling and simulating airport-regional economy system behavior. Model validation using the example of the Xi＇ an airport proved model simulated system behavior accurately. Besides, the result of scenario development simulation provided a new perspective for the practice of airport management and policy-making.
文摘In this paper, we consider the discrete-time dynamic models of input-output whose input-coefficient matrices A. and capital-coefficient matrices Bn can vary with time n. Under certain conditions, we prove that there exists a subspace of nonnegative vectors Ωr such that if initial (input) product , then there is some natural number n0≥0 such that for n≥ n0, the n-th year’sproduct Xn has at least one negative component, which means that economic dislocation occurs.
基金supported by the National Natural Science Foundation of China(No.11304278)the National High-Tech R&D Program(863)of China(No.2014AA093400)
文摘A modeling method is proposed for a dynamic fast steering mirror(FSM) system with dual inputs and dual outputs. A physical model of the FSM system is derived based on first principles, describing the dynamics and coupling between the inputs and outputs of the FSM system. The physical model is then represented in a state-space form. Unknown parameters in the state-space model are identified by the subspace identification algorithm, based on the measured input-output data of the FSM system. The accuracy of the state-space model is evaluated by comparing the model estimates with measurements. The variance-accounted-for value of the state-space model is better than 97%, not only for the modeling data but also for the validation data set, indicating high accuracy of the model. Comparison is also made between the proposed dynamic model and the conventional static model, where improvement in model accuracy is clearly observed. The model identified by the proposed method can be used for optimal controller design for closed-loop FSM systems. The modeling method is also applicable to FSM systems with similar structures.
文摘In this work, several procedures for the fault detection and isolation （FDI） on general aviation aircraft sensors are presented. In order to provide a comprehensive wide-pectrum treatment, both linear and nonlinear, model-based and data-driven methodologies are considered. The main contributions of the paper are related to the development of both FDI polynomial method （PM） and FDI scheme based on the nonLinear geometric approach （NLGA）. As to the PM, the obtained results highlight a good trade-off between solution complexity and resulting performances. Moreover, the proposed PM is especially useful when robust solutions are required for minimising the effects of modelling errors and noise, while maximising fault sensitivity. As to the NLGA, the proposed work is the first development and robust application of the NLGA to an aircraft model in flight conditions characterised by tight-oupled longitudinal and lateral dynamics. In order to verify the robustness of the residual generators related to the previous FDI techniques, the simulation results adopt a typical aircraft reference trajectory embedding several steady-tate flight conditions, such as straight flight phases and coordinated turns. Moreover, the simulations are performed in the presence of both measurement and modelling errors. Finally, extensive simulations are used for assessing the overall capabilities of the developed FDI schemes and a comparison with neural networks （NN） and unknown input Kalman filter （UIKF） diagnosis methods is performed.
