PID-Based Hierarchical Event-Triggered MPC for USV Trajectory Tracking

This article studies the trajectory tracking problem of unmanned surface vehicle(USV), where the USV is required to follow a predefined trajectory accurately. A hierarchical two-layer control architecture is proposed, consisting of an event-triggered model predictive control (ET-MPC) in the upper layer and a discrete PID controller in the lower layer. In particular, the ET-MPC reduces computational load and communication frequency by performing optimization only when the state error exceeds a predefined threshold or the cost function decrease condition is violated. The lower-layer PID loop applies feedforward compensation to decouple linear and angular velocities of the USV and uses a discrete-time difference model to account for the errors caused by actuator motor inertia and nonlinearity. In this way, the ET-MPC method can effectively balance the computational efficiency and performance of the controller. Finally, the hardware experiments are conducted to verify the effectiveness of the proposed method.