Adaptive Nonlinear Model Predictive Control: Maximizing Tire Force and Obstacle Avoidance in Autonomous Vehicles

The ability to reliably maximize tire force usage would improve the safety of autonomous vehicles, especially in challenging edge cases. However, vehicle control near the limits of handling has many challenges, including robustly contending with tire force saturation, balancing model fidelity and computational efficiency, and coordinating inputs with the lower level chassis control system. This work studies nonlinear model predictive control (MPC) for limit handling, specifically adapting to changing tire-road conditions and maximally allocating tire force utilization. We present a novel hierarchical framework that combines a single-track model with longitudinal weight transfer dynamics in the predictive control layer, with lateral brake distribution occurring at the chassis control layer. This vehicle model is simultaneously used in an unscented Kalman filter (UKF) for online friction estimation. Comparative experiments on a full-scale vehicle operating on a race track at up to 95% of maximum tire force usage demonstrate the overall effectiveness of this approach. Finally, emergency obstacle avoidance experiments demonstrate the practical utility of the approach at highway speeds where a blocking obstacle suddenly appears.