模型不确定性下车辆极限操控时基于扩散残差模型预测的转向控制实现车辆稳定
Diffusion-Residual Model Predictive Steering Control for Vehicle Stabilization at the Limit of Handling under Model Uncertainty
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中文总结 AI 辅助
研究在模型不确定性下车辆极限操控时的稳定问题，核心方法是用条件扩散残差模型学习不确定性并应用于控制器参考值和约束，主要贡献是降低峰值侧滑、恢复低摩擦操控方向稳定性，且算法运行高效。
中文摘要 AI 辅助
在极限操控时，稳定的模型预测控制（MPC）依赖于其跟踪的横摆率参考值和所执行的稳定操控包络线，二者均依赖于工作点且先验未知，固定或最坏情况设置要么过于保守要么不安全。我们用条件扩散残差模型学习这种不确定性，并将其应用于控制器的参考值和约束而非控制律。基于转向指令，该模型返回残差均值和预测扩展度：均值调整跟踪的横摆参考值，扩展度在预测时域上传播，通过单边机会回退收紧稳定操控包络线。二者共同构成了所提出的扩散残差MPC（D-res），在跟踪误差出现前就进行预警而非在其出现后通过高增益回路校正。由于每条指令仅需两个矩，生成器离线制表，在线控制器在基线MPC基础上只需添加一次查表操作，且无回路内扩散；在NVIDIA Jetson AGX Xavier上以100Hz运行（最坏情况每步4.08ms）。在包含车辆、轮胎、道路和操控多样性的7自由度模型及高保真CarMaker联合仿真中，D-res降低了固定自行车模型最不准确处的峰值侧滑，并在低摩擦操控中恢复了方向稳定性，此时固定参考值对可用抓地力的指令过大。
英文摘要
At the limit of handling, a stabilizing MPC depends on the yaw-rate reference it tracks and the stable-handling envelope it enforces, both operating-point-dependent and unknown a priori, so fixed or worst-case settings are either too conservative or unsafe. We learn this uncertainty with a conditional diffusion residual model and apply it to the controller's reference and constraints rather than its control law. Conditioned on the steering command, the model returns the residual's mean and a predictive spread: the mean re-sizes the tracked yaw reference, while the spread, propagated over the prediction horizon, tightens the stable-handling envelope through a one-sided chance back-off. Together these form the proposed diffusion-residual MPC (D-res), so caution is anticipated ahead of the tracking error rather than corrected after it by a high-gain loop. Because only two moments per command are needed, the generator is tabulated offline and the online controller adds a single table lookup to the baseline MPC, with no in-loop diffusion; it runs within the 100 Hz budget on an NVIDIA Jetson AGX Xavier (worst-case 4.08 ms per step). Across a 7-DOF model and high-fidelity CarMaker co-simulation spanning vehicle, tire, road, and maneuver diversity, D-res reduces peak side-slip where the fixed bicycle model is least accurate and restores directional stability on low-friction maneuvers, where the fixed reference over-commands the available grip.
发表机构
- Ajou University（韩国亚洲大学）
机构由 AI 辅助整理，请以论文原文为准。
