  In this paper, we propose an event-based sampling policy to implement a
constraint-tightening, robust MPC method. The proposed policy enjoys a
computationally tractable design and is applicable to perturbed, linear
time-invariant systems with polytopic constraints. In particular, the
triggering mechanism is suitable for plants with no centralized sensory node as
the triggering mechanism can be evaluated locally at each individual sensor.
From a geometrical viewpoint, the mechanism is a sequence of hyper-rectangles
surrounding the optimal state trajectory such that robust recursive feasibility
and robust stability are guaranteed. The design of the triggering mechanism is
cast as a constrained parametric-in-set optimization problem with the volume of
the set as the objective function. Re-parameterized in terms of the set
vertices, we show that the problem admits a finite tractable convex program
reformulation and a linear program relaxation. Several numerical examples are
presented to demonstrate the effectiveness and limitations of the theoretical
results.
