In this paper gradient and Hamiltonian dynamics are investigated in both discrete-time and sampled-data contexts. At first, the discrete gradient function is profitably employed to define discrete gradient and Hamiltonian dynamics. On these bases, it is shown that representations of these forms can be recovered when computing the sampled-data equivalent models to gradient and Hamiltonian continuous-time dynamics.
In this paper, we consider the problem of disturbance decoupling for a class of non-minimum-phase nonlinear systems. Based on the notion of partially minimum phaseness, we shall characterize all actions of disturbances which can be decoupled via a static state feedback while preserving stability of the internal residual dynamics. The proposed methodology is then extended to the sampled-data framework via multi-rate design to cope with the rising of the so-called sampling zero dynamics intrinsically induced by classical single-rate sampling.
In this work, how sampling can be instrumental for stabilizing nonlinear dynamics with delays is discussed through several approaches developed by the authors in a comparative perspective with respect to the existing literature. Performances and computational aspects are illustrated through academic examples.
© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma
