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.

Research on Speed Control Methods and Energy-Saving for High-Voltage Transmission Line Inspection Robots along Cable Downhill

“…However, significant research achievements have been made in the automotive field regarding downhill speed control. In the automotive field, various algorithms, such as dual-loop control [11][12][13], neural networks [14,15], and Kalman filtering [16,17], are commonly used to control the running speed of vehicles. Zhang et al implemented speed control for pure electric buses using a dual-loop control system that incorporates both vehicle speed and current feedback [18].…”

Condition-based maintenance strategy for vehicles using hidden Markov models

“…3 Reliabilitycentred optimal maintenance strategy of the equipment has been proposed in Long et al 4 With the concept of the cost function, various maintenance strategies have been evaluated, and specific reliability has been obtained by an optimal maintenance strategy to maintain the operational performance during the lifetime with minimum maintenance cost and total lifetime cost (TLOC). 5 The speed control of an electromagnetic direct drive vehicle robot driver has been done with neural network to obtain driving vehicle with manual transmission and automatic transmission in various test conditions. 6 The performance improvements of pure electric vehicle have been presented by utilization of multi-speed dual-clutch transmissions and continuously variable transmissions.…”