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JRM Vol.35 No.2 pp. 308-316
doi: 10.20965/jrm.2023.p0308
(2023)

Paper:

Yaw-Rate Controller Tuning for Autonomous Driving: Virtual Internal Model Tuning Approach

Motoya Suzuki ORCID Icon and Shuichi Yahagi ORCID Icon

Isuzu Advanced Engineering Center, Ltd.
8 Tsuchidana, Fujisawa, Kanagawa 252-0881, Japan

Received:
September 15, 2022
Accepted:
November 1, 2022
Published:
April 20, 2023
Keywords:
autonomous driving, controller designs, data-driven control, virtual internal model tuning
Abstract

Vehicle yaw-rate control is important for realizing autonomous driving. If the desired yaw-rate response is realized, good autonomous driving can be realized. The gain-scheduled controller should be designed because vehicle has time-variant properties. However, it is difficult to design gain-scheduled controller in the case where vehicle parameters are unknown. To solve this problem, we expand virtual internal model tuning (VIMT) so as to realize desired yaw-rate responses. VIMT can tune the feedback controller by using one-shot experiment data. The processing cost is extremely low because the controller parameter can be obtained by using least square methods. In this study, we verify the validity of the proposed method through vehicle simulator of TruckMaker.

Yaw-rate controller tuning via VIMT

Yaw-rate controller tuning via VIMT

Cite this article as:
M. Suzuki and S. Yahagi, “Yaw-Rate Controller Tuning for Autonomous Driving: Virtual Internal Model Tuning Approach,” J. Robot. Mechatron., Vol.35 No.2, pp. 308-316, 2023.
Data files:
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