Design, Analysis, and Experiments of Preview Path Tracking Control for Autonomous Vehicles

This paper presents a preview steering control algorithm and its closed-loop system analysis and experimental validation for accurate, smooth, and computationally inexpensive path tracking of automated vehicles. The path tracking issue is formulated as an optimal control problem with dynamic disturb...

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Published inIEEE transactions on intelligent transportation systems Vol. 21; no. 1; pp. 48 - 58
Main Authors Xu, Shaobing, Peng, Huei
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Abstract This paper presents a preview steering control algorithm and its closed-loop system analysis and experimental validation for accurate, smooth, and computationally inexpensive path tracking of automated vehicles. The path tracking issue is formulated as an optimal control problem with dynamic disturbance, i.e., the future road curvature. A discrete-time preview controller is then designed on the top of a linear augmented error system, in which the disturbances within a finite preview window are augmented as part of the state vector. The obtained optimal steering control law is in an analytic form and consists of two parts: 1) a feedback control responding to tracking errors and 2) a feedforward control dealing with the future road curvatures. The designed control's nature, capacity, computation load, and underlying mechanism are revealed by the analysis of system responses in the time domain and the frequency domain, theoretical steady-state error, and comparison with the model predictive control (MPC). The algorithm was implemented on an automated vehicle platform, a hybrid Lincoln MKZ. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy, steering smoothness, and computational efficiency compared to the MPC and the full-state feedback control.
AbstractList This paper presents a preview steering control algorithm and its closed-loop system analysis and experimental validation for accurate, smooth, and computationally inexpensive path tracking of automated vehicles. The path tracking issue is formulated as an optimal control problem with dynamic disturbance, i.e., the future road curvature. A discrete-time preview controller is then designed on the top of a linear augmented error system, in which the disturbances within a finite preview window are augmented as part of the state vector. The obtained optimal steering control law is in an analytic form and consists of two parts: 1) a feedback control responding to tracking errors and 2) a feedforward control dealing with the future road curvatures. The designed control’s nature, capacity, computation load, and underlying mechanism are revealed by the analysis of system responses in the time domain and the frequency domain, theoretical steady-state error, and comparison with the model predictive control (MPC). The algorithm was implemented on an automated vehicle platform, a hybrid Lincoln MKZ. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy, steering smoothness, and computational efficiency compared to the MPC and the full-state feedback control.
Author Xu, Shaobing
Peng, Huei
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  surname: Peng
  fullname: Peng, Huei
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  organization: Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
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Snippet This paper presents a preview steering control algorithm and its closed-loop system analysis and experimental validation for accurate, smooth, and...
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SubjectTerms Algorithms
Automation
Autonomous vehicles
Computational modeling
Computer simulation
Control algorithms
Control systems design
Control theory
Feedback control
Feedforward control
Frequency-domain analysis
Heuristic algorithms
Hybrid vehicles
Optimal control
Optimization
Path tracking
Predictive control
Preview control
Roads
Smoothness
State feedback
State vectors
Steering
Systems analysis
Tracking control
Tracking errors
Trajectory
Vehicle dynamics
vehicle dynamics control
Vehicles
Title Design, Analysis, and Experiments of Preview Path Tracking Control for Autonomous Vehicles
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