This paper proposes a non-linear Model Predictive Contouring Control (MPCC) for obstacle avoidance in automated vehicles driven at the limit of handling. The proposed controller integrates motion planning, path tracking and vehicle stability objectives, prioritising obstacle avoidance in emergencies. The controller's prediction model is a non-linear single-track vehicle model with the Fiala tyre to capture the vehicle's non-linear behaviour. The MPCC computes the optimal steering angle and brake torques to minimise tracking error in safe situations and maximise the vehicle-to-obstacle distance in emergencies. Furthermore, the MPCC is extended with the tyre friction circle to fully exploit the vehicle's manoeuvrability and stability. The MPCC controller is tested using real-time rapid prototyping hardware to prove its real-time capability. The performance is compared with a state-of-the-art Model Predictive Control (MPC) in a high-fidelity simulation environment. The double lane change scenario results demonstrate a significant improvement in successfully avoiding obstacles and maintaining vehicle stability.

翻译：本文提出一种用于极限操控下自动驾驶车辆避障的非线性模型预测轮廓控制（MPCC）。该控制器整合了运动规划、路径跟踪与车辆稳定性目标，在紧急情况下优先实现避障功能。控制器的预测模型采用包含Fiala轮胎的非线性单轨车辆模型，以捕捉车辆的非线性动力学特性。MPCC通过计算最优转向角与制动力矩，在安全工况下最小化跟踪误差，并在紧急工况下最大化车辆与障碍物间距。此外，引入轮胎摩擦圆对MPCC进行扩展，以充分利用车辆的机动性与稳定性。通过实时快速原型硬件对MPCC控制器进行测试，验证其实时计算能力。在基于高保真仿真环境的对比实验中，将所提方法与最新模型预测控制（MPC）进行性能比较。双移线工况结果表明，本方法在成功避障与保持车辆稳定性方面具有显著优势。