基于代理模型进化的履带车辆动力学参数优化

To solve the problem of low precision and low efficiency in tracked vehicle dynamics optimization resulting from the weaknesses of traditional agent model construction and application, a parameter optimization method based on surrogate model evolution is proposed. It integrates the optimization iteration process with the dynamic construction process of the surrogate model to reduce the times of invoking the simulation model and hence improve the optimization efficiency. First, based on the vehicle's geometric topology, a multi-body dynamics model considering track envelope effect is constructed. Then, the design space is divided into three-level subspaces. A multi-level fuzzy clustering space reduction method with spatial focus and spatial reduction and not bounded by local optimization is proposed to efficiently reduce the design parameters in the three-level subspaces. Finally, the application is verified by taking the parameter optimization process of the tracked vehicle's multi-body dynamics model as an example. The results show that the multi-body dynamics optimization process of the tracked vehicle under three road conditions reduces the invoking times of simulation model by up to 85%; the comprehensive performance indexes representing tracked vehicle ride comfort and firing accuracy are increased by about 32. 4%, 24. 5% and 20. 4%, respectively. It is proved that this method can effectively improve the efficiency and accuracy of dynamic model optimization.