基于BP神经网络的汽车横摆角速度估计

为估计汽车横摆角速度并提高估计器精度, 采用BP(Back Propagation)神经网络的方法对汽车转向过程的横摆角速度进行估计。现实情况通常存在4 种路面: 干燥路面、沥青路面、积水路面和冰雪路面, 若单纯训练一个网络难以涵盖4 种不同的路面情况。为解决上述问题, 提高网络估计器的精度, 分别在4 种路面工况下训练4 个网络, 构成一个网络组, 再加入网络选择机制, 根据路面情况选择对应的网络的输出值作为横摆角速度的估计值。通过AMESim 与Matlab 联合仿真, 获得网络估计器残差并对估计情况进行分析和评价。该基于数据的方法与基于解析模型的估计方法相比, 不依赖精确的模型, 就能准确估计汽车横摆角速度。仿真结果表明, 基于BP 神经网络的方法对横摆角速度估计是可行的且偏差小, 成本低, 精度高。

Vehicle Yaw Rate Estimation Using BP Neural Networks

In order to estimate the vehicle yaw rate and increase the estimator accuracy, a method of BP(Back Propagation) neural network is adopted to estimate the vehicle yaw rate during the steering condition. There are four kinds of roads that are existing in reality: dry road, pitch road, watered road and ice road, and one neural
network cannot include the condition of four kinds of different roads. To solve the problem and increase the precision of the network estimator, we have trained four neural networks respectively to form a network group. By adding a selecting module to the system, the estimation value of yaw rate with corresponding road friction
coefficient can be picked out. We obtain the residuals of networks by co-simulation of AMESim and MATLAB.Finally, we evaluate and analyse the results of the estimation generating by the estimator. The method adopted is the data-based approach. Compared with the existing model-based method, it is independent of precise model and it can estimate the yaw rate precisely. Simulation results and analysis verified the viability and the precision of using BP neural networks to estimate vehicle yaw rate.