A Computationally Efficient Path-Following Control Strategy of Autonomous Electric Vehicles With Yaw Motion Stabilization
Ningyuan Guo, Xudong Zhang, Yuan Zou, Basilio Lenzo, Tao Zhang
Abstract
This article proposes a computationally efficient path-following control strategy of autonomous electric vehicles (AEVs) with yaw motion stabilization. First, the nonlinear control-oriented model, including path-following model, single-track vehicle model, and magic formula tire model, is constructed. To handle the stability constraints with ease, the nonlinear model predictive control (NMPC) technique is applied for path-following issue. Here, NMPC control problem is reasonably established with the constraints of vehicle sideslip angle, yaw rate, steering angle, lateral position error, and Lyapunov stability. To mitigate the online calculation burden, the continuation/generalized minimal residual (C/GMRES) algorithm is adopted. The dead-zone penalty functions are employed for handling the inequality constraints and holding the smoothness of solution. Moreover, the varying predictive duration is utilized in this article to gain the good initial solution fast by numerical algorithm. Finally, the simulation validations are carried out, which yields that the proposed strategy can achieve desirable path following and vehicle stability efficacy, while greatly reducing the computational burden compared with the NMPC controllers by active set algorithm or interior point algorithm.