Vehicle dynamics control in challenging driving situations using nonlinear model predictive control allocation

This contribution proposes a nonlinear model predictive control allocation algorithm for control of an over-actuated electric vehicle in challenging driving situations. Based on a recently published gradient-based real-time solver, the proposed scheme allows to implement a hierarchical control strategy for lateral and longitudinal control while optimally allocating wheel torques adhering to wheel slip and rate constraints. Incorporating nonlinear tire models enables the decoupled control of vehicle yaw moment and traction force. The performance is demonstrated on a comprehensive vehicle dynamics model.