Development and Evaluation of a Combined Driveline Oscillation and Traction Controller Using Model Predictive Control and Reinforcement Learning: A Comparative Case Study

In electric vehicles, the control of driveline oscillations and tire traction is critical for guaranteeing driver comfort and safety. Yet, achieving sufficient driveline control performance remains challenging in the presence of rapidly varying road conditions. Two promising avenues for further improving driveline control are adaptive model predictive control (MPC) and model-based reinforcement learning (RL). We derive such controllers from the same non-linear vehicle model and validate them through pre-defined test scenarios. The MPC approach employs input and output trajectory tracking with soft constraints to ensure feasible control actions even in the presence of constraint violations and is further supported by a Kalman filter for robust state estimation and prediction. In contrast, the RL controller leverages the model-based DreamerV3 algorithm to learn control policies autonomously, adapting to different road conditions without relying on external information. The results indicate that both controllers achieve comparable overall performance although the MPC solution provides more precise input and output tracking and smoother control inputs, while the RL approach inherently adapts to changing road surfaces, eliminating the need for prior friction knowledge or online friction estimation. We discuss the trade-offs between MPC and RL in terms of complexity, adaptability and performance as well as avenues for future work, such as integrating road-condition estimation into the MPC framework and refining the RL controller for smoother, more precise control action.