机架
计算机科学
汽车工程
扭矩转向
控制(管理)
指导委员会
工程类
机械工程
方向盘
人工智能
工程管理
作者
Chanjung Kim,Dayul Son,Zachary Sabato,Byoungyun Lee
摘要
<div class="section abstract"><div class="htmlview paragraph">Drivers continually require steering performance improvement, particularly in the area of feedback from the road. In this study, we develop a new electrically-assisted steering logic by 1) analyzing existing steering systems to determine key factors, 2) modeling an ideal steering system from which to obtain a desirable driver torque, 3) developing a rack force observer to faithfully represent road information and 4) building a feedback compensator to track the tuned torque. In general, the estimator uses the driver torque, assist torque and other steering system signals. However, the friction of the steering system is difficult to estimate accurately. At high speed, where steering feeling is very important, greater friction results in increased error. In order to solve this problem, we design two estimators generated from a vehicle model and a steering system model. The observer that uses two estimators can reflect various operating conditions by using the strengths of each method. Therefore, it reflects the driving situation more precisely. We evaluated real vehicle performance under various operating conditions to compare the actual rack force and the rack force of the estimator, verifying the accuracy. Simulation-based evaluation and vehicle test confirmed that the proposed logic improves the steering feedback performance, and the robustness to component dispersion.</div></div>
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