Popov-H∞ Robust Path-Tracking Control of Autonomous Ground Vehicles with Consideration of Sector Bounded Kinematic Nonlinearity

2021 ◽  
Author(s):  
Xingyu Zhou ◽  
Zejiang Wang ◽  
Junmin Wang
Keyword(s):  
Longitudinal Velocity ◽  
Path Tracking ◽  
Pole Placement ◽  
Ground Vehicles ◽  
Lane Changing ◽  
Lur’E System ◽  
Vehicle Path ◽  
Lateral Error ◽  
Turning Maneuver ◽  
Bounded Nonlinearity

Abstract This paper proposes a new approach to cope with the kinematic nonlinearity in the H∞ vehicle path-tracking controller synthesis problem. The kinematic nonlinearity presented in the vehicle lateral error state is found to satisfy the sector-bound condition. By isolating the sector bounded nonlinearity via an upper linear fractional transformation (LFT), a Lur'e system is formulated. A nominal robust controller is synthesized to meet both the Popov-H∞ criterion and the regional pole placement requirement. A polytopic gain-scheduling technique is subsequently employed to accommodate the effect of the varying vehicle longitudinal velocity. Finally, an instant-turning maneuver and a sharp lane-changing maneuver are tested in CarSim-Simulink joint simulations whose results demonstrate the superiority of the proposed Popov-H∞ controller over a conventional H∞ controller.

Lateral obstacle avoidance control based on driving behavior recognition of the preceding vehicles in adjacent lanes

2020 ◽  
Vol 68 (10) ◽  
pp. 880-892
Author(s):  
Youguo He ◽  
Xing Gong ◽  
Chaochun Yuan ◽  
Jie Shen ◽  
Yingkui Du
Keyword(s):  
Obstacle Avoidance ◽  
Lateral Displacement ◽  
Longitudinal Velocity ◽  
Driving Behavior ◽  
Lateral Acceleration ◽  
Lateral Motion ◽  
Behavior Recognition ◽  
Dynamic Surface ◽  
Lane Changing ◽  
The Impact

AbstractThis paper proposes a lateral lane change obstacle avoidance constraint control simulation algorithm based on the driving behavior recognition of the preceding vehicles in adjacent lanes. Firstly, the driving behavior of the preceding vehicles is recognized based on the Hidden Markov Model, this research uses longitudinal velocity, lateral displacement and lateral velocity as the optimal observation signals to recognize the driving behaviors including lane-keeping, left-lane-changing or right-lane-changing; Secondly, through the simulation of the dangerous cutting-in behavior of the preceding vehicles in adjacent lanes, this paper calculates the ideal front wheel steering angle according to the designed lateral acceleration in the process of obstacle avoidance, designs the vehicle lateral motion controller by combining the backstepping and Dynamic Surface Control, and the safety boundary of the lateral motion is constrained based on the Barrier Lyapunov Function; Finally, simulation model is built, and the simulation results show that the designed controller has good performance. This active safety technology effectively reduces the impact on the autonomous vehicle safety when the preceding vehicle suddenly cuts into the lane.

Trajectory Generation From Paths for Autonomous Ground Vehicles

2020 ◽  
Author(s):  
Letian Lin ◽  
J. Jim Zhu
Keyword(s):  
Phase Plane ◽  
Longitudinal Velocity ◽  
Initial Boundary ◽  
Limit Function ◽  
Ground Vehicles ◽  
Reverse Time ◽  
Dynamic Constraints ◽  
Autonomous Ground Vehicles ◽  
Practical Concern ◽  
Initial Boundary Condition

Abstract Path-to-trajectory conversion problem for car-like autonomous ground vehicles has been studied in various ways. It is challenging to generate a trajectory which is dynamically feasible for the vehicle and comfortable for the passengers. An important practical concern of low computational costs makes the problem even more difficult. In this work, a path-to-trajectory converter is developed using a novel receding-horizon type suboptimal algorithm. By transforming the dynamic constraints to a longitudinal velocity limit function in the velocity-acceleration phase plane, a time-sub-optimal trajectory satisfying the dynamic constraints and the initial boundary condition is generated by computing the maximum constant acceleration in the down-range horizon. The portion of the trajectory approaching the end of the path is generated in reverse time. As illustrated by some simulation results and validation on a full-scale Kia Soul EV, the proposed path-to-trajectory conversion algorithm is able to account for dynamic constraints of the vehicle and guarantees passenger comfort.

Understanding the Lateral Dimension of Traffic: Measuring and Modeling Lane Discipline

2021 ◽  
pp. 036119812110318
Author(s):  
Rafael Delpiano
Keyword(s):  
New Technologies ◽  
Autonomous Vehicle ◽  
Longitudinal Velocity ◽  
Lateral Position ◽  
Multiple Cameras ◽  
Lateral Velocity ◽  
Lane Changing ◽  
Lateral Dimension ◽  
Vehicle Position ◽  
Longitudinal Position

There is growing interest in understanding the lateral dimension of traffic. This trend has been motivated by the detection of phenomena unexplained by traditional models and the emergence of new technologies. Previous attempts to address this dimension have focused on lane-changing and non-lane-based traffic. The literature on vehicles keeping their lanes has generally been limited to simple statistics on vehicle position while models assume vehicles stay perfectly centered. Previously the author developed a two-dimensional traffic model aiming to capture such behavior qualitatively. Still pending is a deeper, more accurate comprehension and modeling of the relationships between variables in both axes. The present paper is based on the Next Generation SIMulation (NGSIM) datasets. It was found that lateral position is highly dependent on the longitudinal position, a phenomenon consistent with data capture from multiple cameras. A methodology is proposed to alleviate this problem. It was also discovered that the standard deviation of lateral velocity grows with longitudinal velocity and that the average lateral position varies with longitudinal velocity by up to 8 cm, possibly reflecting greater caution in overtaking. Random walk models were proposed and calibrated to reproduce some of the characteristics measured. It was determined that drivers’ response is much more sensitive to the lateral velocity than to position. These results provide a basis for further advances in understanding the lateral dimension. It is hoped that such comprehension will facilitate the design of autonomous vehicle algorithms that are friendlier to both passengers and the occupants of surrounding vehicles.

Active Disturbance Rejection Control for Handling Slip in Tracked Vehicle Locomotion

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Bijo Sebastian ◽  
Pinhas Ben-Tzvi
Keyword(s):  
Disturbance Rejection ◽  
Path Tracking ◽  
Future Research ◽  
Ground Vehicles ◽  
Uneven Terrain ◽  
Active Disturbance Rejection ◽  
Package Delivery ◽  
Disturbance Rejection Control ◽  
Effective Slip ◽  
High Level

This paper describes the use of an active disturbance rejection controller (ADRC) to estimate and compensate for the effect of slip in an online manner to improve the path tracking performance of autonomous ground vehicles (AGVs). AGVs with skid-steer locomotion mode are extensively used for robotic applications in the fields of agriculture, transportation, construction, warehouse maintenance, and mining. Majority of these applications such as performing reconnaissance and rescue operations in rough terrain or autonomous package delivery in urban scenarios, require the system to follow a path predetermined by a high-level planner or based on a predefined task. In the absence of effective slip estimation and compensation, the AGVs, especially tracked vehicles, can fail to follow the path as given out by the high-level planner. The proposed ADRC architecture uses a generic mathematical model that can account for the scaling and shift in the states of the system due to the effects of slip through augmented parameters. An extended Kalman filter (EKF) observer is used to estimate the varying slip parameters online. The estimated parameters are then used to compensate for the effects of slip at each iteration by modifying the control actions given by a low-level path tracking controller. The proposed approach is validated through experiments over flat and uneven terrain conditions including asphalt, vinyl flooring, artificial turf, grass, and gravel using a tracked skid-steer mobile robot. A detailed discussion on the results and directions for future research is also presented.

scholarly journals Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 128233-128249
Author(s):  
Mohammad Rokonuzzaman ◽  
Navid Mohajer ◽  
Saeid Nahavandi ◽  
Shady Mohamed
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Vehicle Path
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