الملخص
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.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Sabaa, M., & Emam, M. (2026). Energy-Aware Path Following: Comparative Analysis of Reinforcement Learning and NMPC for Electric Vehicles. https://omanscience.com/ar/articles/energy-aware-path-following-comparative-analysis-of-reinforcement-learning-and-nmpc-for-electric-vehicles
MLA 9
Sabaa, Mohamed, and Mostafa Emam. "Energy-Aware Path Following: Comparative Analysis of Reinforcement Learning and NMPC for Electric Vehicles." https://omanscience.com/ar/articles/energy-aware-path-following-comparative-analysis-of-reinforcement-learning-and-nmpc-for-electric-vehicles.
شيكاغو (المؤلف–التاريخ)
Sabaa, Mohamed, and Mostafa Emam. 2026. "Energy-Aware Path Following: Comparative Analysis of Reinforcement Learning and NMPC for Electric Vehicles." https://omanscience.com/ar/articles/energy-aware-path-following-comparative-analysis-of-reinforcement-learning-and-nmpc-for-electric-vehicles.
هارفارد
Sabaa, M. and Emam, M. (2026) 'Energy-Aware Path Following: Comparative Analysis of Reinforcement Learning and NMPC for Electric Vehicles', Available at: https://omanscience.com/ar/articles/energy-aware-path-following-comparative-analysis-of-reinforcement-learning-and-nmpc-for-electric-vehicles.
فانكوفر
Sabaa M, Emam M. Energy-Aware Path Following: Comparative Analysis of Reinforcement Learning and NMPC for Electric Vehicles. https://omanscience.com/ar/articles/energy-aware-path-following-comparative-analysis-of-reinforcement-learning-and-nmpc-for-electric-vehicles
IEEE
M. Sabaa, and M. Emam, "Energy-Aware Path Following: Comparative Analysis of Reinforcement Learning and NMPC for Electric Vehicles," https://omanscience.com/ar/articles/energy-aware-path-following-comparative-analysis-of-reinforcement-learning-and-nmpc-for-electric-vehicles.