This paper addresses the problem of the link between the driving style of an ideal driver, modelled as an optimal controller, and fundamental set-up parameters of a vehicle in the GP2 motorsport class. The aim is to evaluate quantitatively how set-up parameters, like distribution of aerodynamic loads, weight and roll stiffness between front and rear axles, affect the driving style, encoded in the shape of the optimal trajectory and in the acceleration, brake and steer inputs.

To this aim, we develop an optimization code that includes a double-track vehicle model capable of solving the minimum lap-time problem (MLTP) on a given track. The track is represented via NURBS curves and the MLTP is framed and solved as an optimal control problem by transcription into a nonlinear program using direct collocation. To assess the accuracy of the vehicle model and the optimization pipeline, we also validate our results against real telemetry data.

The developed software framework lends itself to easily perform both sensitivity analysis and concurrent trajectory planning and set-up parameter optimization: this is obtained by simple promotion of static parameters of interests to variables in the optimal control problem. Some results along these lines are also included.

本文解决了建模为最佳控制器的理想驾驶员的驾驶方式与GP2赛车类车辆的基本设置参数之间的联系问题。目的是定量评估设置参数，例如空气动力负载的分布，前后轴之间的重量和侧倾刚度，如何以最佳轨迹的形状以及加速度，制动和转向输入的形式影响驾驶方式。

为此，我们开发了包括双轨车辆模型的优化代码，该模型能够解决给定轨道上的最小圈速问题（MLTP）。该轨迹通过NURBS曲线表示，而MLTP通过使用直接配置转换为非线性程序来构图和求解，作为最佳控制问题。为了评估车辆模型和优化流水线的准确性，我们还根据真实的遥测数据验证了我们的结果。

所开发的软件框架使其易于执行灵敏度分析以及同时进行的轨迹规划和设置参数优化：这是通过将感兴趣的静态参数简单地提升为最优控制问题中的变量而获得的。这些结果也包括在内。