用于分布式驱动电动汽车直接横摆力矩控制的深度Koopman风险预览监督LTV-MPC
Deep Koopman risk-preview supervised LTV-MPC for direct yaw moment control of distributed drive electric vehicles
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中文总结 AI 辅助
针对分布式驱动电动汽车DYC易在低风险工况下过度干预的问题，提出KRG-LTV-MPC框架，结合深度Koopman模型进行风险门控，在保障稳定性的同时减少了43.9%的横摆力矩干预。
中文摘要 AI 辅助
持续开启的直接横摆力矩控制（DYC）可提升车辆在紧急操纵时的稳定性，但在低风险工况下可能引入不必要的干预。本文提出一种Koopman风险门控线性时变模型预测控制（KRG-LTV-MPC）框架，用于低干预横摆稳定性辅助。该框架未用纯数据驱动的控制预测器替代基于物理的执行模型，而是将基于Koopman的相位风险预览与安全关键执行分离。深度Koopman模型预测侧滑-横摆率相位风险的标称演化，以确定每个采样时刻是否应求解或跳过约束二次规划（QP）问题。当门控激活时，LTV-MPC层计算额外横摆力矩；否则跳过QP，并在有界速率规则下将先前指令的力矩逐渐降至零。事件级影子模式评估显示，在低附着和附着过渡工况下，Koopman预测器提供0.19-0.28秒的正向预警提前时间，而LTV预测器给出延迟预警。在闭环低附着工况下，KRG-LTV-MPC相较于LTV-MPC减少43.9%的累计横摆力矩干预，且仅在41.0%的采样时刻求解QP，同时保持车辆在相位平面内的稳定性。这些结果支持将Koopman相位风险信息作为低干预DYC的智能监督层使用。
英文摘要
Always-on direct yaw moment control (DYC) improves vehicle stability during critical maneuvers but can introduce unnecessary interventions under low-risk conditions. This paper proposes a Koopman risk-gated linear time-varying model predictive control (KRG-LTV-MPC) framework for low-intervention yaw stability assistance. Instead of replacing the physics-based execution model with a fully data-driven control predictor, this framework separates Koopman-based phase-risk preview from safety-critical execution. A Deep Koopman model predicts the nominal evolution of the sideslip-yaw rate phase risk to determine whether the constrained quadratic programming (QP) problem should be solved or skipped at each sampling time. When the gate is active, the LTV-MPC layer calculates the additional yaw moment; otherwise, the QP is skipped and the previously commanded moment is tapered to zero under a bounded-rate rule. Event-level shadow-mode evaluation shows that the Koopman predictor provides positive warning lead times of 0.19-0.28 s under low-friction and friction-transition conditions, whereas the LTV predictor gives delayed warnings. Under closed-loop low-friction conditions, KRG-LTV-MPC reduces the cumulative yaw-moment intervention by 43.9% relative to LTV-MPC and solves the QP for only 41.0% of the samples while maintaining vehicle stability within the phase plane. These results support the use of Koopman phase-risk information as an intelligent supervisory layer for low-intervention DYC.
