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A Computationally Efficient Path Following Control Strategy of Autonomous Electric Vehicles with Yaw Motion Stabilization
IEEE Transactions on Transportation Electrification ( IF 7 ) Pub Date : 2020-06-01 , DOI: 10.1109/tte.2020.2993862
Ningyuan Guo , Xudong Zhang , Yuan Zou , Basilio Lenzo , Tao Zhang

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.

中文翻译:

具有横摆运动稳定功能的自主电动汽车的计算有效路径跟踪控制策略

本文提出了一种具有偏航稳定性的自主电动汽车 (AEV) 的计算高效路径跟随控制策略。首先,构建了非线性控制导向模型,包括路径跟随模型、单轨车辆模型和魔方轮胎模型。为了轻松处理稳定性约束,非线性模型预测控制 (NMPC) 技术被应用于路径跟随问题。在这里,在车辆侧滑角、横摆角速度、转向角、横向位置误差和李雅普诺夫稳定性的约束下,合理建立NMPC控制问题。为了减轻在线计算负担,采用了延续/广义最小残差(C/GMRES)算法。死区惩罚函数用于处理不等式约束和保持解的平滑性。此外,本文利用变化的预测持续时间,通过数值算法快速获得良好的初始解。最后,进行了仿真验证,结果表明所提出的策略可以实现理想的路径跟随和车辆稳定性功效,同时与通过主动集算法或内点算法的 NMPC 控制器相比大大减少了计算负担。
更新日期:2020-06-01
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