Disturbance adaptive steering wheel torque control for enhanced path tracking of autonomous vehicles

2017 ◽  
Author(s):  
Dongpil Lee ◽  
Kyongsu Yi
Keyword(s):  
Autonomous Vehicles ◽  
Path Tracking ◽  
Torque Control ◽  
Steering Wheel ◽  
Wheel Torque

Robust steering-assist torque control of electric-power-assisted-steering systems for target steering wheel torque tracking

Mechatronics ◽  
2018 ◽  
Vol 49 ◽  
pp. 157-167 ◽  
Author(s):  
Dongpil Lee ◽  
Kyongsu Yi ◽  
Sehyun Chang ◽  
Byungrim Lee ◽  
Bongchoon Jang
Keyword(s):  
Electric Power ◽  
Torque Control ◽  
Steering Wheel ◽  
Wheel Torque

Vehicle rollover recovery using active steering/wheel torque control

2008 ◽  
Vol 46 (1) ◽  
pp. 51 ◽  
Author(s):  
Taehyun Shim ◽  
Daniel Toomey ◽  
Chinar Ghike ◽  
Hemant M. Sardar
Keyword(s):  
Torque Control ◽  
Steering Wheel ◽  
Active Steering ◽  
Wheel Torque ◽  
Vehicle Rollover

Steering Wheel Torque Control of Steer-by-Wire System for Steering Feel

2017 ◽  
Author(s):  
Jaepoong Lee ◽  
Sehyun Chang ◽  
Kwangil Kim ◽  
Bongchoon Jang ◽  
Dongpil Lee ◽  
...  
Keyword(s):  
Torque Control ◽  
Steering Wheel ◽  
Steering Feel ◽  
Steer By Wire ◽  
Wheel Torque ◽  
Wire System

scholarly journals Torque Control of Electric Power Steering Systems Based on Improved Active Disturbance Rejection Control

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Shaodan Na ◽  
Zhipeng Li ◽  
Feng Qiu ◽  
Chao Zhang
Keyword(s):  
Electric Power ◽  
Control Method ◽  
Low Frequency ◽  
Torque Control ◽  
Steering Wheel ◽  
Electric Power Steering ◽  
Power Steering ◽  
Wheel Torque ◽  
Steering Torque ◽  
The Impact

In the electric power steering (EPS) system, low-frequency disturbances such as road resistance, irregular mechanical friction, and changing motor parameters can cause steering wheel torque fluctuation and discontinuity. In order to improve the steering wheel torque smoothness, an improved torque control method of an EPS motor is proposed in the paper. A target torque algorithm is established, which is related to steering process parameters such as steering wheel angle and angular speed. Then, a target torque closed-loop control strategy based on the improved ADRC is designed to estimate and compensate the internal and external disturbance of the system, so as to reduce the impact of the disturbance on the steering torque. The simulation results show that the responsiveness and anti-interference ability of the improved ADRC is better than that of the conventional ADRC and PI. The vehicle experiment shows that the proposed control method has good motor current stability, steering torque smoothness, and flexibility when there is low-frequency disturbance.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

A novel path tracking approach considering safety of the intended functionality for autonomous vehicles

2021 ◽  
pp. 095440702110205
Author(s):  
Huiran Wang ◽  
Qidong Wang ◽  
Wuwei Chen ◽  
Linfeng Zhao ◽  
Dongkui Tan
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Coordinated Control ◽  
Steering System ◽  
Front Wheel ◽  
Path Tracking ◽  
Hierarchical Architecture ◽  
Automatic Steering ◽  
The Stability ◽  
Control Layer

To reduce the adverse effect of the functional insufficiency of the steering system on the accuracy of path tracking, a path tracking approach considering safety of the intended functionality is proposed by coordinating automatic steering and differential braking in this paper. The proposed method adopts a hierarchical architecture consisting of a coordinated control layer and an execution control layer. In coordinated control layer, an extension controller considering functional insufficiency of the steering system, tire force characteristics and vehicle driving stability is proposed to determine the weight coefficients of automatic steering and the differential braking, and a model predictive controller is designed to calculate the desired front wheel angle and additional yaw moment. In execution control layer, a H∞ steering angle controller considering external disturbances and parameter uncertainty is designed to track desired front wheel angle, and a braking force distribution module is used to determine the wheel cylinder pressure of the controlled wheels. Both simulation and experiment results show that the proposed method can overcome the functional insufficiency of the steering system and improve the accuracy of path tracking while maintaining the stability of the autonomous vehicle.

An optimal wheel-torque control on a compliant modular robot for wheel-slip minimization

Robotica ◽  
2015 ◽  
Vol 35 (2) ◽  
pp. 463-482 ◽  
Author(s):  
Avinash Siravuru ◽  
Suril V. Shah ◽  
K. Madhava Krishna
Keyword(s):  
Friction Coefficient ◽  
Normal Force ◽  
Experimental Studies ◽  
Traction Force ◽  
Static Stability ◽  
Wheel Speed ◽  
Torque Control ◽  
Modular Robot ◽  
Wheel Slip ◽  
Wheel Torque

SUMMARYThis paper discusses the development of an optimal wheel-torque controller for a compliant modular robot. The wheel actuators are the only actively controllable elements in this robot. For this type of robots, wheel-slip could offer a lot of hindrance while traversing on uneven terrains. Therefore, an effective wheel-torque controller is desired that will also improve the wheel-odometry and minimize power consumption. In this work, an optimal wheel-torque controller is proposed that minimizes the traction-to-normal force ratios of all the wheels at every instant of its motion. This ensures that, at every wheel, the least traction force per unit normal force is applied to maintain static stability and desired wheel speed. The lower this is, in comparison to the actual friction coefficient of the wheel-ground interface, the more margin of slip-free motion the robot can have. This formalism best exploits the redundancy offered by a modularly designed robot. This is the key novelty of this work. Extensive numerical and experimental studies were carried out to validate this controller. The robot was tested on four different surfaces and we report an overall average slip reduction of 44% and mean wheel-torque reduction by 16%.

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