Online Adaptive Platoon Control for Connected and Automated Vehicles via Physics Enhanced Residual Learning

TL;DR

This paper presents a physics enhanced residual learning framework for connected and automated vehicle platoon control, combining physics-based models with neural network learning to improve accuracy, adaptability, and efficiency in dynamic scenarios.

Contribution

The novel PERL framework integrates physical vehicle dynamics with neural network residual learning for improved platoon control performance.

Findings

Significant reduction in position and speed errors in simulations and robot tests.

Enhanced adaptability and rapid convergence under external disturbances.

Outperforms pure physical and learning models in accuracy and efficiency.

Abstract

This paper introduces a physics enhanced residual learning (PERL) framework for connected and automated vehicle (CAV) platoon control, addressing the dynamics and unpredictability inherent to platoon systems. The framework first develops a physics-based controller to model vehicle dynamics, using driving speed as input to optimize safety and efficiency. Then the residual controller, based on neural network (NN) learning, enriches the prior knowledge of the physical model and corrects residuals caused by vehicle dynamics. By integrating the physical model with data-driven online learning, the PERL framework retains the interpretability and transparency of physics-based models and enhances the adaptability and precision of data-driven learning, achieving significant improvements in computational efficiency and control accuracy in dynamic scenarios. Simulation and robot car platform tests demonstrate that PERL significantly outperforms pure physical and learning models, reducing average cumulative absolute position and speed errors by up to 58.5% and 40.1% (physical model) and 58.4% and 47.7% (NN model). The reduced-scale robot car platform tests further validate the adaptive PERL framework's superior accuracy and rapid convergence under dynamic disturbances, reducing position and speed cumulative errors by 72.73% and 99.05% (physical model) and 64.71% and 72.58% (NN model). PERL enhances platoon control performance through online parameter updates when external disturbances are detected. Results demonstrate the advanced framework's exceptional accuracy and rapid convergence capabilities, proving its effectiveness in maintaining platoon stability under diverse conditions.