Self-adaptive Torque Vectoring Controller Using Reinforcement Learning

TL;DR

This paper demonstrates that reinforcement learning, specifically Deep Deterministic Policy Gradient, can adaptively tune torque-vectoring controllers to improve vehicle stability across various driving conditions, outperforming traditional tuning methods.

Contribution

It introduces a reinforcement learning approach for real-time parameter tuning of torque-vectoring controllers, enhancing vehicle stability in diverse and extreme driving environments.

Findings

RL-based tuning outperforms genetic algorithms and trial-and-error methods.

The approach maintains stability under low friction and high velocity conditions.

Simulation results show significant improvement in vehicle stability.

Abstract

Continuous direct yaw moment control systems such as torque-vectoring controller are an essential part for vehicle stabilization. This controller has been extensively researched with the central objective of maintaining the vehicle stability by providing consistent stable cornering response. The ability of careful tuning of the parameters in a torque-vectoring controller can significantly enhance vehicle's performance and stability. However, without any re-tuning of the parameters, especially in extreme driving conditions e.g. low friction surface or high velocity, the vehicle fails to maintain the stability. In this paper, the utility of Reinforcement Learning (RL) based on Deep Deterministic Policy Gradient (DDPG) as a parameter tuning algorithm for torque-vectoring controller is presented. It is shown that, torque-vectoring controller with parameter tuning via reinforcement learning performs well on a range of different driving environment e.g., wide range of friction conditions and different velocities, which highlight the advantages of reinforcement learning as an adaptive algorithm for parameter tuning. Moreover, the robustness of DDPG algorithm are validated under scenarios which are beyond the training environment of the reinforcement learning algorithm. The simulation has been carried out using a four wheels vehicle model with nonlinear tire characteristics. We compare our DDPG based parameter tuning against a genetic algorithm and a conventional trial-and-error tunning of the torque vectoring controller, and the results demonstrated that the reinforcement learning based parameter tuning significantly improves the stability of the vehicle.