Abstract
Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the MPC controller can achieve the best tracking of the reference path. The MPC problem is formulated as a linear time-varying (LTV) MPC controller to achieve a predictive model from nonlinear vehicle dynamics to continuous online linearization. The MPC-PFT controller method performs well by increasing the effective length of the reference path. Compared with MPC and PFT controllers, the effectiveness and robustness of the proposed method are proved by simulations of two typical working conditions.
Highlights
Due to the worldwide sustained growth in the number of vehicles, it is urgent to reduce traffic accidents caused by the improper operation of the driver and to make the road safe [1]
It was proposed that a driver model using optimal preview control is a useful and direct method for representing closed-loop automobile driving by CC
Combining the predicted vehicle speed and the optimal preview curvature determined by using the Preview-follower theory (PFT) with the predicted state variable, the reference yaw rate is derived on every sampling point
Summary
Due to the worldwide sustained growth in the number of vehicles, it is urgent to reduce traffic accidents caused by the improper operation of the driver and to make the road safe [1]. There are many investigations of trajectory tracking, the steering control is still a challenging area involving how to balance vehicle lateral stability and tracking accuracy. Based on the driver–vehicle–road closed-loop system, driver model is the most critical crux related to the performance of trajectory tracking. It was proposed that a driver model using optimal preview control is a useful and direct method for representing closed-loop automobile driving by CC. Guo and P.S. Fancher for modeling the behavior of drivers in the tracking process [8]. The theory describes that the driver’s operation in a path tracking system is always aimed at stabilizing a vehicle along a desired path. R.S. Sharp proposed an autonomous vehicle steering mathematical model based on linear optimal discrete time preview control theory [9]. Guo et al [10]
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