AI 中文总结
研究针对赛车运动应用提出最小圈时规划框架，考虑状态干扰和参数不确定性，扩展公式并采用简约激活策略，通过MPC验证，相比无鲁棒性规划，鲁棒规划失败运行少、关键信号分散紧，提升了可跟踪性。
AI 中文摘要
本研究提出并验证了一种用于赛车运动应用的最小圈时规划（MLTP）框架，该框架对状态干扰和参数不确定性都具有鲁棒性。该方法基于先前的干扰感知框架，在每个赛道点，在短时间范围内传播随机车辆动力学，并根据地平线末端的最坏情况收紧轮胎摩擦约束。我们扩展了公式以考虑关键车辆参数的不确定性：转动惯量、质心位置和空气阻力系数。为了使扩展后的公式在计算上易于处理，一种空间选择性、简约的激活策略将鲁棒约束限制在最关键的赛道段。我们通过使用模型预测控制器（MPC）作为虚拟测试驾驶员来证明鲁棒参考的改进驾驶性能。对于每个参考，相同的MPC在具有代表性的巴塞罗那-加泰罗尼亚赛道段上驱动模拟的FSAE（方程式SAE）赛车进行1000次运行，随机实现脉冲干扰和参数散射。我们将无鲁棒性规划的标称参考与鲁棒对应物进行比较。后者产生的失败运行始终较少，并且以适度的赛段时间成本，关键信号（车辆输入、车轴饱和度）在参考值周围的分散更紧密，这证明了更好的可跟踪性。
英文摘要
This study presents and validates a minimum-lap-time planning (MLTP) framework for motorsport applications that embeds robustness against both state disturbances and parameter uncertainty. The methodology builds upon a prior disturbance-aware framework that, at each track point, propagates stochastic vehicle dynamics over a short horizon and tightens tyre-friction constraints based on the worst-case scenario at horizon end. We extend the formulation to account for uncertainty in key vehicle parameters: moment of inertia, centre-of-mass position, and aerodynamic drag coefficient. To keep the extended formulation computationally tractable, a spatially selective, parsimonious activation strategy confines the robust constraints to the circuit segments where they are most critical. We demonstrate the improved driveability of the robust references by employing a model predictive controller (MPC) as a virtual test driver. For each reference, the same MPC drives a simulated FSAE (Formula SAE) car over 1000 runs on a representative Barcelona-Catalunya sector, with randomly realised impulsive disturbances and parameter scatter. We compare a nominal reference, planned without robustness, against its robust counterparts. The latter yield consistently fewer failed runs and, at a moderate sector-time cost, show tighter dispersion of key signals (vehicle inputs, axle saturations) around the reference values, evidence of better trackability.
