Giovanni Zappa

TL;DR: Predictive regulation of linear discrete-time systems subject to unknown but bounded disturbances and to state/control constraints and an algorithm based on constraint restrictions is presented and its stability properties are analysed.

TL;DR: A novel approach based on minimum-volume bounding parallelotopes is introduced and an algorithm of polynomial complexity is derived to address the problem of recursively estimating the state uncertainty set of a discrete-time linear dynamical system.

TL;DR: A recursive procedure providing an approximation of the parameter set of interest through parallelotopes is presented, and an efficient algorithm is proposed that is similar to that of the commonly used ellipsoidal approximation schemes.

TL;DR: A procedure for the recursive approximation of the feasible parameter set of a linear model with a set membership uncertainty description is provided and several approximation strategies for polytopes are presented.

TL;DR: By increasing the control horizon length, the proposed algorithm, referred to by the acronym MUSMAR, is shown to be a natural generalization of standard self-tuning controllers and closely approximates a steady-state LQG controller inheriting the intrinsic robustness of LQGs design.