  This paper presents a novel set-based model predictive control for tracking,
which provides the largest domain of attraction, even with the minimal
predictive/control horizon. The formulation - which consists of a single
optimization problem - shows a dual behavior: one operating inside the maximal
controllable set to the feasible equilibrium set, and the other operating at
the N-controllable set to the same equilibrium set. Based on some finite-time
convergence results, asymptotic stability of the resulting closed-loop is
proved, while recursive feasibility is ensured for any change of the setpoint.
The properties and advantages of the proposal have been tested on simulation
models.
