A novel event-triggered H ∞-type robust model predictive control (RMPC) approach for linear systems with disturbances is proposed in this paper. The H ∞-type cost function is employed in our design, which renders a straightforward design of the triggering condition concerning the closed-loop stability. The resultant control system is input-to-state stable with an optimized disturbance attenuation level. Comparing with the time-triggered scenarios, the proposed event-triggered RMPC strategy significantly reduces the computational burden. The Zeno-like behavior together with system errors is further considered, which facilitates the practical implementation of the proposed method. The simulations reveal that the event-triggered H ∞-type RMPC approach has satisfying control performance, yet the computing time is significantly reduced. INDEX TERMS Robust model predictive control, event-triggered control, input-to-state stability, H ∞-type cost function, Zeno-like behavior.

A robust H∞-type state feedback model predictive control (H∞-SFMPC) with input constraints is proposed to optimize the control performance during the ship sailing. Specifically, the approach employed in this paper is able to optimize the closed-loop performance with respect to an H∞-type cost function which predicts the system performance based on the actual model instead of the ideal model. As a result, the effect caused by disturbances is attenuated. The state feedback control gain for the control input of the rudder-fin joint roll/yaw control system is obtained by solving a constrained convex optimization problem in terms of linear matrix inequalities. Simulations are carried out, which reveal that the proposed approach has outstanding control performance. Furthermore, it is found that the approach also has significant robustness with respect to parameter uncertainties.

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