汽车驾驶机器人模糊神经网络换挡控制方法

为了实现汽车驾驶机器人挡位决策的智能化,提出了一种驾驶机器人模糊神经网络换挡控制方法.模糊神经网络模型的输入为驾驶机器人油门机械腿的位移、试验车辆的车速和加速度,模型的输出为挡位.输入变量的隶属函数都为3个,类型都采用广义钟形函数gbellmf,网络训练算法选用反向传播算法和最小二乘法相结合的混合学习算法.仿真结果表明,汽车驾驶机器人模糊神经网络控制仿真挡位与试验挡位基本一致,该方法可根据操作工况环境实现正确的汽车驾驶机器人挡位控制.

Shift Control Method of Vehicle Robot Driver Based on Fuzzy Neural Network

In order to realize the intelligent shift of robot driver, a shift control method of vehicle robot driver based on fuzzy neural network (FNN) was proposed. The displacement of throttle pedal for robot driver, speed and acceleration of test vehicle were used as the input of FNN model, and shift was used as the output of FNN model. The number of membership functions was three, and the type of membership functions was gbellmf (generalized bell membership function). The hybrid learning algorithm that combined back propagation algorithm with least square method was applied to train the model. Simulation results demonstrated that the results of simulation shift for robot driver using FNN control had a good consistency with the results of experimental shift. Furthermore, the proposed method could realize the gear-selection of robot driver correctly with the changes of operation environment.