Neural network–based speed control method and experimental verification for electromagnetic direct drive vehicle robot driver

Abstract

The article presents an application of neural network to an electromagnetic direct drive vehicle robot driver. To achieve the accuracy and adaptability of speed control for driving vehicle with manual transmission and automatic transmission in various test conditions, a new neural network–based speed control method of the direct drive vehicle robot driver is presented. The mechanical actuators of the direct drive vehicle robot driver are directly driven through electromagnetic linear actuator. The coordinated control system structure of the direct drive vehicle robot driver is described. The mechanical actuators include the driving mechanical legs and the shift manipulator of the direct drive vehicle robot driver. The driving mechanical legs are consisted of the throttle mechanical leg, the brake mechanical leg, and the clutch mechanical leg. The displacement of the mechanical actuators represent the displacement of the driving mechanical legs, and the displacement of the shift manipulator. The number of the network hidden node is five, and a tangent transfer function is used as the neurons transfer function. The actual vehicle speed is as the variable of the network output layer, and a linear transfer function is as the neurons transfer function. The neural network training algorithm adopts the Levenberg–Marquardt training algorithm. The vehicle tests manipulated by the robot driver in different driving conditions are conducted. The proposed control method of the robot driver is verified and compared with the performances of other control method and human driver.