Abstract
For the firing accuracy problem of the moving tank with different speed and different road level, the modelling and optimization method of the projectile disturbances of the moving tank under the general driving conditions were studied. A high-dimensional multi-objective optimization method for the projectile disturbances of the moving tank considering the different driving conditions was proposed based on the sequential approximate optimization method. Therein, a projectile disturbance multi-disciplinary simulation model of the moving tank considering multiple nonlinear factors was built and the sliced Latin hypercube design method was introduced to apply the experimental design. On the basis, the BP neural network surrogate model of the projectile disturbances at muzzle-exit of the moving tank under the general driving conditions was established. With the genetic algorithm and the potential optimal criterion, the sample points were updated to improve the accuracy of the surrogate model during the optimization. The optimization results show that the projectile disturbances at muzzle-exit of the moving tank under the general driving conditions were reduced effectively, and the comprehensive firing accuracy of the moving tank was improved. The research can provide a reference for realizing the reasonable match between firing accuracy and mobility of the tank.
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This work was supported by the National Natural Science Foundation of China (grant number 11572158, 51705253).
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Recommended by Editor No-cheol Park
Yu Chen is a Lecturer of the School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China. He received his Ph.D. in Nanjing University of Science and Technology. His research interests include numerical simulation, vibration and control, and dynamics analysis.
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Chen, Y., Yang, G., Zhou, H. et al. Sequential approximate optimization on projectile disturbances of the moving tank based on BP neural network. J Mech Sci Technol 35, 935–944 (2021). https://doi.org/10.1007/s12206-021-0206-z
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DOI: https://doi.org/10.1007/s12206-021-0206-z