Abstract
This paper proposes the extended state observer (ESO)-based active disturbance rejection control (ADRC) for the speed tracking of an autonomous vehicle. Uncertainties, both in the vehicle plant and in the sensors, such as nonlinear uncertainties due to the powertrain dynamics, variations in rolling resistance and air resistance, are all estimated in real-time by an extended state observer (ESO). Furthermore, a simple vehicle longitudinal dynamics model, including a mean value engine model (MVEM), is implemented to obtain the parameters in ADRC and design a feedforward controller to enhance the controller’s performance. The proposed controller is validated through CarSim®/Simulink® simulations and road tests. The simulation validates the adaptiveness of the proposed controller against the well-tuned proportional integral derivative (PID) controller, and the speed tracking error of the proposed controller is within 1.26% in simulation. Simulation results also show that fuel consumption can be improved by 3.6% by changing the accelerator pedal depth and positive rate. Finally, the road tests are completed under four kinds of road conditions, and the maximum tracking error is smaller than 0.5 km/h.
Highlights
Autonomous vehicle technology has received extensive attention because it has the potential to reduce the number of road fatalities, reduce fuel consumption, and improve transportation efficiency.Significant progress has been made in the fields of sensing, computer hardware, and software technology over the past few decades
The incline of the road, which was obtained from a high-definition map, was known in the simulation
In the interests of safety, the road tests for the longitudinal controller we developed were carried out on the autonomous driving test field at Tianjin University
Summary
Autonomous vehicle technology has received extensive attention because it has the potential to reduce the number of road fatalities, reduce fuel consumption, and improve transportation efficiency.Significant progress has been made in the fields of sensing, computer hardware, and software technology over the past few decades. Vehicle Future Challenge (IVFC) [3] and the World Intelligent Driving Challenge (WIDC) [4] have driven academic research and the development of associated technologies in China. This paper describes the design of the longitudinal controller for Tianjin University’s autonomous vehicle which competed in the World Intelligent Driving Challenge competition. The architecture of an autonomous driving system typically consists of three layers: The environmental perception layer, the decision-making and planning layer, and the driving control layer [5]. The driving control layer is responsible for tracking the desired trajectory, which is obtained from the decision-making and planning layer. The lateral and longitudinal controllers are vital components of the driving control layer. The lateral controller adjusts the steering wheel of the vehicle to track the target trajectory.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
