A Stable Analytical Solution Method for Car-Like Robot Trajectory Tracking and Optimization

Keyvan Majd; Mohammad Razeghi-Jahromi; Abdollah Homaifar

doi:10.1109/JAS.2019.1911816

Volume 7 Issue 1

Jan. 2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(1): 39-47

Keyvan Majd, Mohammad Razeghi-Jahromi and Abdollah Homaifar, "A Stable Analytical Solution Method for Car-Like Robot Trajectory Tracking and Optimization," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 39-47, Jan. 2020. doi: 10.1109/JAS.2019.1911816

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Keyvan Majd, Mohammad Razeghi-Jahromi and Abdollah Homaifar, "A Stable Analytical Solution Method for Car-Like Robot Trajectory Tracking and Optimization," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 39-47, Jan. 2020. doi: 10.1109/JAS.2019.1911816

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A Stable Analytical Solution Method for Car-Like Robot Trajectory Tracking and Optimization

doi: 10.1109/JAS.2019.1911816

Funds: This work was partially supported by the Air Force Research Laboratory and Office of the Secretary of Defense (OSD) (FA8750-15-2-0116), the US Department of Transportation (USDOT), and Research and Innovative Technology Administration (RITA) under University Transportation Center (UTC) Program (DTRT13-G-UTC47)

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Author Bio:
Keyvan Majd received the B.S. degree in electrical engineering from Ferdowsi University of Mashhad, Mashhad, Iran, in 2015, and the M.S. degree in electrical engineering from North Carolina A&T State University, Greensboro, NC, in 2019. He is currently pursuing the Ph.D. degree in Computer Science at Arizona State University, Tempe, AZ. His research interests include motion planning of autonomous vehicles, optimal control theory and model predictive control

Mohammad Razeghi-Jahromi received the B.S. degree from Amirkabir University of Technology, Tehran, Iran, in 1997, the M.S. degrees from University of Tehran, Tehran, Iran in 2000 and University of Rochester, Rochester, NY, USA in 2012, respectively and the Ph.D. degree from University of Rochester, Rochester, NY, USA in 2016, all in electrical and computer engineering. He joined ABB Corporate Research Center United States in 2017, where he is currently a Research Scientist. His main areas of research interest include control systems theory, optimization, stochastic process, statistical signal processing, and machine learning

Abdollah Homaifar received B.S. and M.S. degrees from the State University of New York at Stony Brook in 1979 and 1980, respectively, and his Ph.D. degree from the University of Alabama in 1987, all in electrical engineering. He is the NASA Langley Distinguished Professor and the Duke Energy Eminent Professor in the Department of Electrical and Computer Engineering at North Carolina A&T State University (NCA&TSU). He is the Director of the Autonomous Control and Information Technology Institute and the Testing, Evaluation, and Control of Heterogeneous Large-scale Systems of Autonomous Vehicles (TECHLAV) Center at NCA & TSU. His research interests include machine learning, unmanned aerial vehicles (UAVs), testing and evaluation of autonomous vehicles, optimization, and signal processing. He also serves as an Associate Editor of the Journal of Intelligent Automation and Soft Computing and is a Reviewer for IEEE Transactions on Fuzzy Systems, Man Machines and Cybernetics, and IEEE Transactions on Neural Networks. He is a Member of the IEEE Control Society, Sigma Xi, Tau Beta Pi, and Eta Kapa Nu
Corresponding author: A. Homaifar is with the Department of Electrical and Computer Engineering, North Carolina A&T State University, Greensboro, NC 27403 USA (e-mail: homaifar@ncat.edu)
Received Date: 2019-04-26
Revised Date: 2019-08-13
Accepted Date: 2019-09-26

Abstract

Abstract

In this paper, the car-like robot kinematic model trajectory tracking and control problem is revisited by exploring an optimal analytical solution which guarantees the global exponential stability of the tracking error. The problem is formulated in the form of tracking error optimization in which the quadratic errors of the position, velocity, and acceleration are minimized subject to the rear-wheel car-like robot kinematic model. The input-output linearization technique is employed to transform the nonlinear problem into a linear formulation. By using the variational approach, the analytical solution is obtained, which is guaranteed to be globally exponentially stable and is also appropriate for real-time applications. The simulation results demonstrate the validity of the proposed mechanism in generating an optimal trajectory and control inputs by evaluating the proposed method in an eight-shape tracking scenario.
- Global asymptotic stability,
- input-output linearization,
- optimal control,
- trajectory tracking

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References

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The car-like robot kinematic model trajectory tracking, and control problem is revisited.
A globally exponentially stable trajectory optimization and tracking framework is proposed.
The analytical solution using variational approach is proposed to solve the trajectory optimization problem.
The method can tackle the optimal trajectory optimization of higher dimensional kinematic models.

A Stable Analytical Solution Method for Car-Like Robot Trajectory Tracking and Optimization

doi: 10.1109/JAS.2019.1911816

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通讯作者: 陈斌, bchen63@163.com

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