Experimental Validation of Event-Triggered Model Predictive Control for Autonomous Vehicle Path Tracking

Abstract

This paper presents an experimental validation of an event-triggered model predictive control (MPC) for autonomous vehicle (AV) path-tracking control using real-world testing. Path tracking is a critical aspect of AV control, and MPC is a popular control method for this task. However, traditional MPC requires extensive computational resources to solve real-time optimization problems, which can be challenging to implement in the real world. To address this issue, event-triggered MPC, which only solves the optimization problem when a triggering event occurs, has been proposed in the literature to reduce computational requirements. This paper then conducts experimental validation, where event-triggered MPC is compared to traditional time-triggered MPC through real-world testing, and the results demonstrate that the event-triggered MPC method not only offers a significant reduction in computation compared to timetriggered MPC but also improves the control performance.