In this paper, the problem of steering control is investigated for vehicle path tracking in the presence of parametric uncertainties and nonlinearities. In practice, the vehicle mass varies due to the number of passengers or amount of payload, while the vehicle velocity also changes during normal cruising, which significantly influences vehicle dynamics. Moreover, the vehicle dynamics are strongly nonlinear caused by the tire/road forces under different road surface conditions. With fuzzy modeling method, the original nonlinear path tracking system with parameter variations is first formulated as a T-S fuzzy model with additive norm-bounded uncertainties, and then an approach to the fuzzy observer-based output feedback steering control for vehicle dynamics is proposed under a fuzzy Lyapunov function framework. By employing matrix inequality convexifying techniques, a sufficient condition is developed in the form of linear matrix inequalities such that the closed-loop path tracking error system is asymptotically stable with a guaranteed H∞ level. Finally, the effectiveness of the proposed fuzzy observer-based output feedback controller is demonstrated in Carsim/Matlab joint simulation environment, via which the advantage of a T-S fuzzy observer-based output controller over the closed-loop driver model embedded in Carsim is also shown with parametric uncertainties and nonlinearities.

中文翻译：

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一种新颖的基于模糊观测器的自动驾驶车辆路径跟踪转向控制方法


本文研究了存在参数不确定性和非线性的情况下车辆路径跟踪的转向控制问题。实际上，车辆质量会因乘客数量或有效负载量而变化，而车辆速度在正常巡航期间也会发生变化，这会显着影响车辆动力学。此外，在不同路面条件下，由轮胎/路面力引起的车辆动力学具有很强的非线性。采用模糊建模方法，首先将参数变化的原始非线性路径跟踪系统表述为具有加性范数有界不确定性的TS模糊模型，然后提出一种基于模糊观测器的车辆动力学输出反馈转向控制方法。模糊李亚普诺夫函数框架。通过采用矩阵不等式凸化技术，以线性矩阵不等式的形式提出了充分条件，使得闭环路径跟踪误差系统在保证的H∞水平下渐近稳定。最后，在Carsim/Matlab联合仿真环境中证明了所提出的基于模糊观测器的输出反馈控制器的有效性，通过该仿真环境还显示了基于TS模糊观测器的输出控制器相对于Carsim中嵌入的闭环驱动器模型的优势具有参数不确定性和非线性。