非线性MPC在自动驾驶赛车中的动态约束紧缩：通过收缩分析
Dynamic Constraint Tightening for Nonlinear MPC for Autonomous Racing via Contraction Analysis
AI总结：
本文提出了一种基于收缩分析的非线性MPC框架，用于自动驾驶赛车在操控极限下的路径跟踪，通过优化CCM实现约束紧缩，提高计算效率并增强鲁棒性。
摘要：
本文开发了一种稳健的非线性模型预测控制（MPC）框架，用于自动驾驶车辆在操控极限下的路径跟踪，该框架基于一个扰动的单轨模型所衍生的控制收缩度量（CCM）。我们首先提出了适用于自动驾驶车辆的非线性MPC方案。在此基础上，我们假设轮胎参数存在有限的不确定性，以及在横向和纵向方向上的有界力扰动。通过简化扰动模型，我们优化了用于不确定模型的CCM，并通过在车辆性能动态极限下的仿真进行了验证。此CCM随后用于参数化MPC公式中的同型管，用于约束紧缩。所得到的稳健非线性MPC在计算上比竞争方法更高效，因为它仅在预测模型中引入了一个额外的状态变量，相比名义方案。仿真结果表明，同型管在名义方案可能违反约束的区域中扩展最显著，展示了其能够捕捉所有不确定轨迹的能力，同时避免不必要的保守性。
英文摘要：
This work develops a robust nonlinear Model Predictive Control (MPC) framework for path tracking in autonomous vehicles operating at the limits of handling utilizing a Control Contraction Metric (CCM) derived from a perturbed dynamic single track model. We first present a nonlinear MPC scheme for autonomous vehicles. Building on this nominal scheme, we assume limited uncertainty in tire parameters as well as bounded force disturbances in both lateral and longitudinal directions. By simplifying the perturbed model, we optimize a CCM for the uncertain model, which is validated through simulations at the dynamic limits of vehicle performance. This CCM is subsequently employed to parameterize a homothetic tube used for constraint tightening within the MPC formulation. The resulting robust nonlinear MPC is computationally more efficient than competing methods, as it introduces only a single additional state variable into the prediction model compared to the nominal scheme. Simulation results demonstrate that the homothetic tube expands most significantly in regions where the nominal scheme would otherwise violate constraints, illustrating its ability to capture all uncertain trajectories while avoiding unnecessary conservatism.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
