Real-time Motion Control with Iterative Optimization and Robustness Analysis for Autonomous Driving

被引：0

作者：

Liu, Zhichao ^{[1
]}

Lee, Duong ^{[2
]}

Zhang, Kai ^{[2
,3
]}

Zhang, Bin ^{[1
]}

机构：

[1] Univ South Carolina, Dept Elect Engn, Columbia, SC 29208 USA

[2] Amer Haval Motor Technol, Farmington Hills, MI USA

[3] Argo AI, Harrisburg, PA USA

来源：

2019 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM) | 2019年

关键词：

D O I：

10.1109/aim.2019.8868413

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, a real-time motion controller with iterative optimization and robustness analysis is proposed for an autonomous vehicle. The motion controller is divided into lateral and longitudinal controllers. In this study, this study only considers the lateral controller in which the design is based on linear quadratic regulator (LQR) algorithm and a look-up table is applied to accelerate the computation for varying longitudinal velocity. A complete robustness analysis using Monte Carlo method is carried out to test the motion controller's robustness on parameter uncertainties. Additionally, an iterative control simulation is proposed to adapt the control maneuver command such that the controller's tracking performance is improved. Simulation results are provided to show the effectiveness of the proposed motion control strategy.

引用

页码：1385 / 1390

页数：6

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