Development of a robotic driver for autonomous vehicle following

Proceedings of the Institution of Mechanical Engineers, Part D:

2020

In this paper, a steering robust control method based on professional driver behavior with modeling uncertainties and external disturbance is proposed for an unmanned driving robotic vehicle, to realize the accurate and stable steering control like a professional human driver. An unmanned driving robotic vehicle nonlinear dynamics model considering modeling uncertainties and external disturbance is established. A driver behavior model composed of an adaptive preview model, a driver path planning model, and a driver desired yaw rate model is established, with the influences on preview time and driver path planning strategy, respectively. On the basis of this, a robust steering controller based on professional driver behavior is presented, by taking road information, driver reaction delay time, and vehicle driving status as inputs and the servo motor rotation angle of the steering mechanical arm as output. The stability of the control system with modeling uncertainties and external disturbance is proved. A comparison of the analysis results of simulation and experiment among the proposed control method, other existing control methods, and professional human driver demonstrates the effectiveness of the proposed method.

Section: Introductionmentioning

confidence: 99%

Driver-behavior-based robust steering control of unmanned driving robotic vehicle with modeling uncertainties and external disturbance

Proceedings of the Institution of Mechanical Engineers, Part D:

2020

“…3 As a driving device of the vehicle robot driver, the servo electric drive needs a mechanism of rotary motion into linear motion. 4,6 Electromagnetic linear motor can solve the shortcomings of three other drive styles. It can improve the transmission efficiency and transmission accuracy and make the transmission mechanism simple.…”

Section: Introductionmentioning

confidence: 99%

Multibody dynamics modeling of electromagnetic direct-drive vehicle robot driver

International Journal of Advanced Robotic Systems

Zhang

2017

Collaborative dynamics modeling of flexible multibody and rigid multibody for an electromagnetic direct-drive vehicle robot driver is proposed in the article. First, spatial dynamic equations of the direct-drive vehicle robot driver are obtained based on multibody system dynamics. Then, the shift manipulator dynamics model and the mechanical leg dynamics model are established on the basis of the multibody dynamics equations. After establishing a rigid multibody dynamics model and conducting finite element mesh and finite element discrete processing, a flexible multibody dynamics modeling of the electromagnetic direct-drive vehicle robot driver is established. The comparison of the simulation results between rigid and flexible multibody is performed. Simulation and experimental results show the effectiveness of the presented model of the electromagnetic direct-drive vehicle robot driver.

“…2,3 Vehicle robot driver is a robot which can be equipped in a vehicle cab without any modification. 4 It 5 The drive way of the vehicle robot driver includes hydraulic drive, pneumatic drive, and servo electric drive. It is steady for hydraulic drive.…”

Section: Introductionmentioning

confidence: 99%

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

Advances in Mechanical Engineering

Zhang

2017

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.