引用本文:陈虹,郭洋洋,刘俊,等.基于驾驶状态预测的人机力矩协同转向控制器设计[J].控制与决策,2019,34(11):2390-2396
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基于驾驶状态预测的人机力矩协同转向控制器设计
陈虹1,2, 郭洋洋2, 刘俊2, 郭洪艳2, 崔茂源3
(1. 吉林大学汽车仿真与控制国家重点实验室,长春130022;2. 吉林大学通信工程学院, 长春130022:3. 中国第一汽车股份有限公司智能网联开发院,长春130013)
摘要:
针对人机协同转向控制中对于驾驶员参与和驾驶员状态考虑较少这一问题,提出一种基于驾驶员状态预测的人机力矩协同(human-vehicle torque collaborative based on driver state prediction,HVTC-DSP)转向控制方法.该方法以力矩为人机交互接口,提高了驾驶员的参与程度;同时,在控制器设计过程中采用模型预测控制方法,将驾驶员状态考虑在内,对驾驶员状态进行预测.采用高精度车辆仿真软件veDYNA进行仿真验证,结果表明,与不考虑驾驶员状态的人机协同力矩(human-vehicle torque collaborative based on no driver state prediction,HVTC-NDSP)转向控制方法相比,所提方法可以使辅助力矩更好地跟随驾驶员动作,提高车辆转向性能,减小侧向位移偏差,同时对不同驾驶员也有较好的适应性.进而,以驾驶员下一步动作为参考,使驾驶员当前力矩尽可能接近下一步期望的力矩,在转向性能几乎不受影响的情况下,适当减轻驾驶员操作负担.
关键词:  驾驶员状态预测  两点预瞄驾驶员模型  主动转向  模型预测控制  人机交互  力矩接口
DOI:10.13195/j.kzyjc.2019.0575
分类号:TP273
基金项目:国家自然科学基金国际(地区)合作与交流项目(61520106008);国家自然科学基金重大项目(61790563);汽车振动噪声与安全控制综合技术国家重点实验室开放基金项目(W65-GNZX-2018-024).
Design of human-vehicle torque collaborative steering controller based on driving state prediction
CHEN Hong1,2,GUO Yang-yang2,LIU Jun2,GUO Hong-yan2,CUI Mao-yuan3
(1. State Key Laboratory of Automotive Simulation and Control,Jilin University, Changchun130022,China;2. College of Communication Engineering,Jilin University,Changchun130022,China;3. Intelligent Connected Vehicle Development Institute,FAW Group Co. Ltd,Changchun130013,China)
Abstract:
Aiming at the problem that driver's participation and driver's state are seldom considered in human-vehicle collaborative steering control, a human-vehicle torque collaborative steering control method based on driver state prediction is proposed. This method takes torque as human-vehicle interface to improve the degree of driver's participation. At the same time, in the process of controller design, taking the driver state into account, the model predictive control method is used, to predict the state of the driver. veDYNA, a high-precision vehicle simulation software, is adopted for simulation verification. The results show that compared with the human-vehicle torque collaborative based on no driver state prediction steering control method, the proposed method can make the auxiliary torque better follow driver's actions, improve vehicle steering performance, and reduce lateral displacement deviation. At the same time, it has good adaptability to different drivers. Furthermore, with the driver's next action as the reference, the current torque can be as close as possible to the desired torque in the next step. Under the condition that the steering performance is almost unaffected, the driver's operating burden can be appropriately reduced.
Key words:  driver state prediction  two point visual driver model  active steering  model predictive control  human-vehicle interaction  torque interface

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