  This paper studies integral-type event-triggered model predictive control
(MPC) of continuous-time nonlinear systems. An integral-type event-triggered
mechanism is proposed by incorporating the integral of errors between the
actual and predicted state sequences, leading to reduced average sampling
frequency. Besides, a new and improved robustness constraint is introduced to
handle the additive disturbance, rendering the MPC problem with a potentially
enlarged initial feasible region. Furthermore, the feasibility of the designed
MPC and the stability of the closed-loop system are rigorously investigated.
Several sufficient conditions to guarantee these properties are established,
which is related to factors such as the prediction horizon, the disturbance
bound, the triggering level, and the contraction rate for the robustness
constraint. The effectiveness of the proposed algorithm is illustrated by
numerical examples and comparisons.
