一种高效的无模型设置用于纵向和横向车辆控制。通过互联的pro-SiVIC/RTMaps原型化平台进行验证
An efficient model-free setting for longitudinal and lateral vehicle control. Validation through the interconnected pro-SiVIC/RTMaps prototyping platform
AI总结：
本文提出一种高效的无模型控制方法，用于车辆纵向和横向运动跟踪，通过实际数据和虚拟数据验证其有效性。
摘要：
本文针对车辆纵向和横向运动跟踪问题，提出了一种无模型控制方法，以克服参数不确定性和路面相互作用力等困难。纵向和横向运动作为输出变量，而驾驶/制动扭矩和转向轮角度作为输入变量。本文通过实际数据和虚拟数据验证了该方法的有效性，并与非线性展平控制和经典PID控制进行了比较。
英文摘要：
In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is addressed. Let us point out that a "good" modeling is often quite difficult or even impossible to obtain. It is due for example to parametric uncertainties, for the vehicle mass, inertia or for the interaction forces between the wheels and the road pavement. To overcome this type of difficulties, we consider a model-free control approach leading to "intelligent" controllers. The longitudinal and the lateral motions, on one hand, and the driving/braking torques and the steering wheel angle, on the other hand, are respectively the output and the input variables. An important part of this work is dedicated to present simulation results with actual data. Actual data, used in Matlab as reference trajectories, have been previously recorded with an instrumented Peugeot 406 experimental car. The simulation results show the efficiency of our approach. Some comparisons with a nonlinear flatness-based control in one hand, and with a classical PID control in another hand confirm this analysis. Other virtual data have been generated through the interconnected platform SiVIC/RTMaps, which is a virtual simulation platform for prototyping and validation of advanced driving assistance systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
