arXiv：cs.LG· Mohamed Sabaa, Mostafa Emam·· 6 小时前AI 评分26
电动车能量感知路径跟踪：强化学习与 NMPC 对比分析
Energy-Aware Path Following: Comparative Analysis of Reinforcement Learning and NMPC for Electric Vehicles
AI 导读
一项研究在同一 Frenet 坐标系运动学车辆模型下，对比了 NMPC、PPO、增益调度 Ackermann 状态反馈（PID-SF）和 Stanley 几何基线四种控制器，并采用含显式再生制动的 VT-CPEM 能量模型。
正文
Abstract:Path-following control strategies typically follow the bi-objective optimization dilemma: minimizing deviations from a reference path while maintaining smooth speed profiles. The latter objective is especially relevant for Electric Vehicles (EVs), since their limited driving range can be extended by recovering energy through regenerative braking, a feature that has not yet been sufficiently studied in the literature. In this work, we perform a comparative analysis of four controllers under one common Frenet frame-based kinematic vehicle model, utilizing a validated energy model (VT-CPEM) with explicit regenerative braking. Herein, we implement the following controllers: Nonlinear Model Predictive Control (NMPC), Proximal Policy Optimization (PPO), gain-scheduled Ackermann state-feedback baseline (PID-SF), and a Stanley geometric baseline. To satisfy real-time requirements, we implement the NMPC using JIT-compiled CasADi. Moreover, we train the PPO using traditional straight and S-curve tracks, after which we successfully transfer the unmodified policy to unseen tracks, including: an ISO 3888-1 lane-change, a chicane, randomly-generated parameterized-splines, and a $\pm3^\circ$ graded road. In addition, the policy transfers to a dynamic single-track vehicle model with linear tires, zero-shot with an acceptable initial performance, which was optimized after brief fine-tuning. Thereby, we demonstrate that our PPO is readily transferable to more comprehensive vehicle models. We conclude with a performance analysis of developed controllers and discuss ideas for future work.
| Comments: | 20 pages, 12 figures, currently submitted for review at the journal (Robotics and Autonomous Systems) this https URL |
| Subjects: | Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2610.08112 [cs.RO] |
| (or arXiv:2610.08112v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08112 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mostafa Emam [view email]
[v1]
Tue, 6 Oct 2026 10:35:13 UTC (241 KB)
来源：arXiv：cs.LG · arxiv.org