Conventional trajectory optimization techniques have been challenged by their inability to handle threats with irregular shapes and the tendency to be sensitive to control variations of aircraft. Aiming to overcome th...Conventional trajectory optimization techniques have been challenged by their inability to handle threats with irregular shapes and the tendency to be sensitive to control variations of aircraft. Aiming to overcome these difficulties, this paper presents an alternative approach for trajectory optimization, where the problem is formulated into a parametric optimization of the maneuver variables under a tactics template framework. To reduce the size of the problem, global sensitivity analysis （GSA） is performed to identify the less-influential maneuver variables. The probability collectives （PC） algorithm, which is well-suited to discrete and discontinuous optimization, is applied to solve the trajectory optimization problem. The robustness of the trajectory is assessed through multiple sampling around the chosen values of the maneuver variables. Meta-models based on radius basis function （RBF） are created for evaluations of the means and deviations of the problem objectives and constraints. To guarantee the approximation accuracy, the meta-models are adaptively updated during optimization. The proposed approach is demonstrated on a typical airground attack mission scenario. Results reveal that the proposed approach is capable of generating robust and optimal trajectories with both accuracy and efficiency.展开更多
Traditional Global Sensitivity Analysis（GSA） focuses on ranking inputs according to their contributions to the output uncertainty.However,information about how the specific regions inside an input affect the output ...Traditional Global Sensitivity Analysis（GSA） focuses on ranking inputs according to their contributions to the output uncertainty.However,information about how the specific regions inside an input affect the output is beyond the traditional GSA techniques.To fully address this issue,in this work,two regional moment-independent importance measures,Regional Importance Measure based on Probability Density Function（RIMPDF） and Regional Importance Measure based on Cumulative Distribution Function（RIMCDF）,are introduced to find out the contributions of specific regions of an input to the whole output distribution.The two regional importance measures prove to be reasonable supplements of the traditional GSA techniques.The ideas of RIMPDF and RIMCDF are applied in two engineering examples to demonstrate that the regional moment-independent importance analysis can add more information concerning the contributions of model inputs.展开更多
基金supported by Open Research Foundation of Science and Technology on Aerospace Flight Dynamics Laboratory (No. 2012afd1010)
文摘Conventional trajectory optimization techniques have been challenged by their inability to handle threats with irregular shapes and the tendency to be sensitive to control variations of aircraft. Aiming to overcome these difficulties, this paper presents an alternative approach for trajectory optimization, where the problem is formulated into a parametric optimization of the maneuver variables under a tactics template framework. To reduce the size of the problem, global sensitivity analysis （GSA） is performed to identify the less-influential maneuver variables. The probability collectives （PC） algorithm, which is well-suited to discrete and discontinuous optimization, is applied to solve the trajectory optimization problem. The robustness of the trajectory is assessed through multiple sampling around the chosen values of the maneuver variables. Meta-models based on radius basis function （RBF） are created for evaluations of the means and deviations of the problem objectives and constraints. To guarantee the approximation accuracy, the meta-models are adaptively updated during optimization. The proposed approach is demonstrated on a typical airground attack mission scenario. Results reveal that the proposed approach is capable of generating robust and optimal trajectories with both accuracy and efficiency.
基金supported by the National Natural Science Foundation of China(No.NSFC51608446)the Fundamental Research Fund for Central Universities of China(No.3102016ZY015)
文摘Traditional Global Sensitivity Analysis（GSA） focuses on ranking inputs according to their contributions to the output uncertainty.However,information about how the specific regions inside an input affect the output is beyond the traditional GSA techniques.To fully address this issue,in this work,two regional moment-independent importance measures,Regional Importance Measure based on Probability Density Function（RIMPDF） and Regional Importance Measure based on Cumulative Distribution Function（RIMCDF）,are introduced to find out the contributions of specific regions of an input to the whole output distribution.The two regional importance measures prove to be reasonable supplements of the traditional GSA techniques.The ideas of RIMPDF and RIMCDF are applied in two engineering examples to demonstrate that the regional moment-independent importance analysis can add more information concerning the contributions of model inputs.
