Robust model predictive ship heading control with event-triggered strategy

Heading control is crucial for achieving reliable path following of autonomous ships. To ensure the control accuracy and improve the real-time performance and energy efficiency, a robust heading control algorithm based on Event-Triggered (ET) and Model Predictive Control (MPC) is studied. To reduce the frequency of steering, an ET mechanism with taking the heading error as the trigger condition is designed. Then, a predictive steering model with taking rudder as an input and heading as an output is built in combination of the trigger mechanism, a Nomoto model with disturbances. An objective function considering multiple constraints, energy consumption, and heading following performance is designed, and a dynamic optimization problem is solved with a nonlinear and hybrid optimization function. Simulation results show that the proposed event-triggered model predictive control (ETMPC) heading controller has better real-time and robustness performance than conventional MPC and PID, respectively.