Shared Control Strategy Based on Driver’s Trajectory Following Intention

2021 ◽  
Author(s):  
Lanei Abi ◽  
Dafeng Jin ◽  
Liangyao Yu
Keyword(s):  
Control Strategy ◽  
Shared Control ◽  
Trajectory Following

Shared control strategy based on trajectory following and driver intention optimization

2021 ◽  
pp. 095440702098772
Author(s):  
Lanie Abi ◽  
Dafeng Jin ◽  
Cenbo Xiong ◽  
Xiaohui Liu ◽  
Liangyao Yu
Keyword(s):  
Optimization Algorithm ◽  
Control Strategy ◽  
Transfer Mechanism ◽  
Shared Control ◽  
Lateral Stability ◽  
Flexible Control ◽  
Control Transfer ◽  
Trajectory Following ◽  
Driver’S Intention ◽  
Braking Process

During the emergency braking process on the split-[Formula: see text] road, the lateral stability of the vehicle is poor, and the intervention of ABS will cause corresponding lateral disturbance. It is difficult for the driver to control the vehicle accurately. Especially at the end of the braking process, due to the withdrawal of ABS, the increase in braking pressure causes the longitudinal force of the tires on both sides to be inconsistent, which reduces the stability of the vehicle at this time. This paper proposed a shared control strategy to solve the related problems. First, a segmented active steering strategy is used in the driver’s intention optimization algorithm to optimize the driver’s actions in time at the initial stage of the braking process and to optimize the lateral stability of the vehicle by tracking the estimated tire slip angle at the end of the braking process. Then, according to the path envelope based on the driver’s path error neglecting feature and the dynamic state of the vehicle, a flexible control transfer mechanism is established. The trajectory following algorithm based on linear quadratic regulator is used to correct the driver’s intention optimization algorithm according to the flexible control transfer mechanism.

scholarly journals A Wheeled Inverted Pendulum Learning Stable and Accurate Control from Demonstrations

2019 ◽  
Vol 9 (24) ◽  
pp. 5279
Author(s):  
Shaokun Jin ◽  
Yongsheng Ou
Keyword(s):  
Control Strategy ◽  
Inverted Pendulum ◽  
Target Position ◽  
Path Following ◽  
Learning Model ◽  
Human Control ◽  
Wheeled Inverted Pendulum ◽  
Learning From Demonstrations ◽  
Trajectory Following ◽  
Robot Position

In order to enable robots to be more intelligent and flexible, one way is to let robots learn human control strategy from demonstrations. It is a useful methodology, in contrast to traditional preprograming methods, in which robots are required to show generalizing capacity in similar scenarios. In this study, we apply learning from demonstrations on a wheeled, inverted pendulum, which realizes the balance controlling and trajectory following simultaneously. The learning model is able to map the robot position and pose to the wheel speeds, such that the robot regulated by the learned model can move in a desired trajectory and finally stop at a target position. Experiments were undertaken to validate the proposed method by testing its capacity of path following and balance guaranteeing.

scholarly journals Evaluation of Telerobotic Shared Control Strategy for Efficient Single-Cell Manipulation

2012 ◽  
Vol 9 (2) ◽  
pp. 402-406 ◽  
Author(s):  
Jungsik Kim ◽  
Hamid Ladjal ◽  
David Folio ◽  
Antoine Ferreira ◽  
Jung Kim
Keyword(s):  
Single Cell ◽  
Control Strategy ◽  
Shared Control ◽  
Cell Manipulation ◽  
Single Cell Manipulation

scholarly journals Dynamic External Force Feedback Loop Control of a Robot Manipulator Using a Neural Compensator—Application to the Trajectory Following in an Unknown Environment

2009 ◽  
Vol 19 (1) ◽  
pp. 113-126 ◽  
Author(s):  
Farid Ferguene ◽  
Redouane Toumi
Keyword(s):  
External Force ◽  
Control Strategy ◽  
Feedback Loop ◽  
Force Feedback ◽  
Position Control ◽  
Robot Manipulator ◽  
Suggested Approach ◽  
Loop Control ◽  
Unknown Environment ◽  
Trajectory Following

Dynamic External Force Feedback Loop Control of a Robot Manipulator Using a Neural Compensator—Application to the Trajectory Following in an Unknown EnvironmentForce/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies proposed in the literature is external force feedback loop control. It fully employs the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. The performance of this control strategy is affected by uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. We show that this control strategy is robust with respect to payload uncertainties, position and environment stiffness, and dry and viscous friction. Simulation results for a three degrees-of-freedom manipulator and various types of environments and trajectories show the effectiveness of the suggested approach compared with classical external force feedback loop structures.

Shared Control Strategy for Vehicle Stability on U-Split Road

2019 ◽  
Author(s):  
Liangyao Yu ◽  
Lanie Abi ◽  
Zhenghong Lu ◽  
Yaqi Dai
Keyword(s):  
Control Strategy ◽  
Response Speed ◽  
Steering System ◽  
Stability Control ◽  
Front Wheel ◽  
Shared Control ◽  
Steering Wheel ◽  
Vehicle Dynamic ◽  
Stability Performance ◽  
The Stability

Abstract The steer-by-wire (SBW) system eliminates the mechanical connection between the steering wheel and the carriage wheel. It eliminates various limitations of the traditional steering system, so that the steering ratio of the car can be freely designed and the steering by wire system can achieve good active front wheel steering (AFS) function. In the study of the stability control of vehicles on the μ-split road, there are mainly two methods, one based on vehicle trajectory maintenance and the other based on vehicle dynamic stability control. Both of these control methods have delays, which is not conducive to the trajectory flowing ability of the vehicle when driving on the μ-split road. A shared control strategy is proposed to improve the vehicle’s stability. The purpose of this study is to establish different variable transmission ratio characteristic curves according to the different input signals of the driver and the vehicle, such as angular change speed, steering wheel angle, etc. Based on these conditions, a new model combining driver’s intention with vehicle dynamic model is established, so as to achieve the purpose of judging the stability of vehicle in advance, to reduce the delay time of control and to improve the response speed, which will improve the stability performance of the vehicle.

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