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David Lackner

dblp:274/9297 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 since 2021Systems, architecture and hardware · 2 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 50% Autonomous driving · 50%
Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Autonomous driving
autonomous ground vehicle
0.412020
Slip-Based Nonlinear Recursive Backstepping Path Following Controller for Autonomous Ground Vehicles · ICRA 2020
Robotics › Motion planning and robot control
path following
0.412020
Slip-Based Nonlinear Recursive Backstepping Path Following Controller for Autonomous Ground Vehicles · ICRA 2020
Robotics › Motion planning and robot control › mobile robot control
steering control
0.412020
Slip-Based Nonlinear Recursive Backstepping Path Following Controller for Autonomous Ground Vehicles · ICRA 2020
Robotics › Autonomous driving
vehicle control
0.412020
Slip-Based Nonlinear Recursive Backstepping Path Following Controller for Autonomous Ground Vehicles · ICRA 2020
Mathematical optimization › control theory
model predictive control
0.112020
Slip-Based Nonlinear Recursive Backstepping Path Following Controller for Autonomous Ground Vehicles · ICRA 2020

Methods — techniques the papers use, named apart from their topics

variable structure control · 0.9backstepping control · 0.9high-gain observer · 0.4high gain observer · 0.4
YearPublicationVenuePosition
2021 Continuous Robust Trajectory Tracking Control for Autonomous Ground Vehicles Considering Lateral and Longitudinal Kinematics and Dynamics via Recursive Backstepping
abstract
Maintaining lateral and longitudinal trajectory tracking accuracy is challenging for autonomous ground vehicles (AGVs). This paper considers kinematics and dynamics of longitudinal and lateral motion to form a novel composite structure considering the cross-impacts of acceleration and steering commands on tracking errors in the lateral and longitudinal directions, respectively. The multi-tiered structure uses backstepping with smooth robust control to iteratively map kinematics-based velocity and yaw rate commands to slip-yaw dynamics-based acceleration and steering commands. In kinematics, longitudinal tracking error is stabilized by sliding mode control (SMC) while variable structure control (VSC) stabilizes lateral tracking error and balances tracking accuracy and steering gracefulness. Backstepping extends these commands through vehicle dynamics to provide robust steering and acceleration commands. Cross impacts between lateral and longitudinal motion is addressed by vehicle modeling and controller designs. A robust observer is applied for sideslip estimation to reject uncertainties. Peaking from the high gain observer and robust control is addressed. Stability analysis is provided and field experiments on an open road demonstrate and validate effectiveness of the controllers.
Ming Xin 0002, David Lackner, Zhongchao Ren, Mark A. Minor
IROS4
2020 Slip-Based Nonlinear Recursive Backstepping Path Following Controller for Autonomous Ground Vehicles
abstract
Path following accuracy and error convergence with graceful motion in vehicle steering control is challenging due to the competing nature of these requirements, especially across a range of operating speeds. This work is founded upon slip-based kinematic and dynamic models, which allow derivation of controllers considering error due to sideslip and the mapping between steering commands and graceful lateral motion. A novel recursive backstepping steering controller is proposed that better couples yaw-rate based path following commands to steering angle and rate. Observer based sideslip estimates are combined with heading error in the kinematic controller to provide feedforward slip compensation. Path following error is compensated by a Variable Structure Controller (VSC) to balance graceful motion, path following error, and robustness. Yaw rate commands are used by a backstepping dynamic controller to generate robust steering commands. A High Gain Observer (HGO) estimates sideslip and yaw rate for output feedback control. Stability analysis is provided and peaking is addressed. Field experimental results evaluate the work and provide comparisons to MPC.
Ming Xin 0002, David Lackner, Mark A. Minor
ICRA3