Event-triggered Model Predictive Control for Autonomous Vehicle with Rear Steering
2022-01-1059
03/29/2022
- Event
- Content
- This paper proposes a new model predictive control design for autonomous vehicle path tracking problem. The vehicle is equipped with active rear steering and the propulsion system is assumed to be controlled by separate controller. Traditional nonlinear model prediction control (NMPC) has been shown to provide satisfactory control performance in this problem. However, the high throughput of NMPC limits its implementation in production vehicle. To address this issue, we propose a novel event triggered NMPC formulation, where the NMPC is triggered only when the actual states deviate from prediction beyond certain threshold. When NMPC is not triggered, the optimal control sequence computed from last triggering event is shifted to determine the control action. Simulation is conducted using bicycle model as prediction model, and the AV is set to follow a sinusoidal reference path with propulsion torque. Numerical results show significant throughput reduction of event triggered NMPC while maintaining comparable control performance. In particular, event triggered NMPC requires only 50% of computational efforts compared to traditional NMPC, thus improving its real-time implementability with production grade electronic control unit (ECU).
- Citation
- huang, S., and Chen, J., "Event-triggered Model Predictive Control for Autonomous Vehicle with Rear Steering," SAE Technical Paper 2022-01-1059, 2022, .