  In this paper, we propose a new aperiodic formulation of model predictive
control for nonlinear continuous-time systems. Unlike earlier approaches, we
provide event-triggered conditions without using the optimal cost as a Lyapunov
function candidate. Instead, we evaluate the time interval when the optimal
state trajectory enters a local set around the origin. The obtained
event-triggered strategy is more suitable for practical applications than the
earlier approaches in two directions. First, it does not include parameters
(e.g., Lipschitz constant parameters of stage and terminal costs) which may be
a potential source of conservativeness for the event-triggered conditions.
Second, the event-triggered conditions are necessary to be checked only at
certain sampling time instants, instead of continuously. This leads to the
alleviation of the sensing cost and becomes more suitable for practical
implementations under a digital platform. The proposed event-triggered scheme
is also validated through numerical simulations.
