Affiliation:
1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, PR China
2. Nanjing Chenguang Group Co., Ltd., Nanjing, PR China
3. Inner Mongolia North Heavy Industries Group Co., Ltd., Baotou, PR China
Abstract
To enhance the hitting accuracy of tank with moving firing, an uncertain optimization method based on stochastic programming is adopted to reduce the initial disturbance of projectile. Firstly, the firing dynamics model of moving tank is modeled to simulate the initial disturbance of projectile. Secondly, the controllable interior ballistic parameters such as projectile structure parameters and propellant parameters are treated as random design variables. The surrogate model for the firing dynamics model of moving tank is constructed by using the neural network based on deep learning. The uncertain optimization problem is transformed into a deterministic optimization problem by stochastic programming method. Then multi-objective genetic algorithm is adopted to settle optimization model, and reasonable design interval of random design variables is obtained. Finally, a six-degree-of-freedom rigid external ballistics model is used to establish a hitting accuracy evaluation model of moving tank based on interval uncertainty analysis. Through this model, the effectiveness of the optimization method is demonstrated.
Funder
Jiangsu Planned Projects for Postdoctoral Research Funds
Natural Science Foundation of Jiangsu Province
Nanjing University of Science and Technology Independent Research Program
China National Postdoctoral Program for Innovative Talents
Nanjing Municipal Human Resources and Social Security Bureau
China Postdoctoral Science Foundation
National Natural Science Foundation of China