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CLC number: TP242.6

On-line Access: 2012-08-02

Received: 2011-12-26

Revision Accepted: 2012-05-03

Crosschecked: 2012-07-06

Cited: 4

Clicked: 10634

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.8 P.593-600


An iterative linear quadratic regulator based trajectory tracking controller for wheeled mobile robot

Author(s):  Hao-jie Zhang, Jian-wei Gong, Yan Jiang, Guang-ming Xiong, Hui-yan Chen

Affiliation(s):  Intelligent Vehicle Research Center, Beijing Institute of Technology, Beijing 100081, China

Corresponding email(s):   haojie.bit@gmail.com, gjwmit@gmail.com

Key Words:  Lattice planner, Global trajectory, Kinematic model, Trajectory tracking controller, Iterative linear quadratic regulator (ILQR)

Hao-jie Zhang, Jian-wei Gong, Yan Jiang, Guang-ming Xiong, Hui-yan Chen. An iterative linear quadratic regulator based trajectory tracking controller for wheeled mobile robot[J]. Journal of Zhejiang University Science C, 2012, 13(8): 593-600.

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T1 - An iterative linear quadratic regulator based trajectory tracking controller for wheeled mobile robot
A1 - Hao-jie Zhang
A1 - Jian-wei Gong
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A1 - Guang-ming Xiong
A1 - Hui-yan Chen
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1100379

We present an iterative linear quadratic regulator (ILQR) method for trajectory tracking control of a wheeled mobile robot system. The proposed scheme involves a kinematic model linearization technique, a global trajectory generation algorithm, and trajectory tracking controller design. A lattice planner, which searches over a 3D (x, y, θ) configuration space, is adopted to generate the global trajectory. The ILQR method is used to design a local trajectory tracking controller. The effectiveness of the proposed method is demonstrated in simulation and experiment with a significantly asymmetric differential drive robot. The performance of the local controller is analyzed and compared with that of the existing linear quadratic regulator (LQR) method. According to the experiments, the new controller improves the control sequences (v, ω) iteratively and produces slightly better results. Specifically, two trajectories, ‘S’ and ‘8’ courses, are followed with sufficient accuracy using the proposed controller.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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