Ricerc@Sapienza

This paper represents a first attempt toward an alternative way of computing reduction-based feedback à la Arstein for input-delayed systems. To this end, we first exhibit a new reduction state evolving as a new dynamics which is free of delays. Then, feedback design is carried out by enforcing passivity-based arguments in the reduction time-delay scenario. The case of strict-feedforward dynamics serves as a case study to discuss in details the computational advantages. A simulated exampled highlights performances.

In this letter, the reduction method is extended to time-delay systems affected by two mismatched input delays. To this end, the intrinsic feedback structure of the retarded dynamics is exploited to deduce a reduced dynamics which is free of delays. Moreover, among other possibilities, an Immersion and Invariance feedback over the reduced dynamics is designed for achieving stabilization of the original systems. A chained sampled-data dynamics is used to show the effectiveness of the proposed control strategy through simulations.

In this paper, the problem of stabilizing linear port-controlled Hamiltonian dynamics through interconnection and damping assignment in presence of input delays is considered. The contribution exploits the reduction approach allowing to reveal and shape the energy properties of the time-delay dynamics. Performances are illustrated on a simple mechanical system.

In this paper, the problem of stabilizing nonlinear discrete-time systems affected by delayed state measures is addressed under average passivity-based control. The contribution stands in the introduction of a new delay-free dynamics which is used for the design of the stabilizing feedback. Simulations over an academic example illustrate the performances in a comparative sense.

In this paper, we propose continuous-time and sampled-data output feedback controllers for nonlinear multi-input multi-output systems with time-varying measurement and input delays, with no restriction on the bound or serious limitations on the growth of the nonlinearities. A state prediction is generated by chains of saturated high-gain observers with switching error-correction terms and the state prediction is used to stabilize the system with saturated controls. The observers reconstruct the unmeasurable states