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.

The paper discusses stabilization of nonlinear discrete-time dynamics in feedforward form. First it is shown how to define a Lyapunov function for the uncontrolled dynamics via the construction of a suitable cross-term. Then, stabilization is achieved in terms of u-average passivity. Several constructive cases are analyzed.

The concept of partially minimum phase systems is introduced and used with reference to the class of nonlinear systems exhibiting a linear output. It turns out that input-output feedback linearization with stability of the internal dynamics can be pursued via the use of a dummy output with respect to which the system is minimum-phase. The design strategy is extended to multirate sampled-data control and a working example illustrates the performances.

This paper deals with the dynamic Euclidean Multivehicle Routing Problem (MVRP) in both deterministic and uncertain scenarios. The objective of MVRP is to find the best paths for a fleet of vehicles that are cooperative to visit a set of targets.

In this paper, we present a novel solution for real-time, Non-Linear Model Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The proposed controller formulates the Optimal Control Problem (OCP) in terms of flat outputs over an adaptive lattice. In common approximated OCP solutions, the number of discretization points composing the lattice represents a critical upper bound for real-time applications. The proposed NMPC-based technique refines the initially uniform time horizon by adding time steps with a sampling criterion that aims to reduce the discretization error.

The paper is concerned with black-box nonlinear constrained multi-objective optimization problems. Our interest is the definition of a multi-objective deterministic partition-based algorithm. The main target of the proposed algorithm is the solution of a real ship hull optimization problem. To this purpose and in pursuit of an efficient method, we develop an hybrid algorithm by coupling a multi-objective DIRECT-type algorithm with an efficient derivative-free local algorithm.

In this paper, we analyze some theoretical properties of the problem of minimizing a quadratic function with a cubic regularization term, arising in many methods for unconstrained and constrained optimization that have been proposed in the last years. First we show that, given any stationary point that is not a global solution, it is possible to compute, in closed form, a new point with a smaller objective function value. Then, we prove that a global minimizer can be obtained by computing a finite number of stationary points.

Mathematical modeling represents a useful instrument to study the evolution of an epidemic spread and to determine the best control strategy to reduce the number of infected subjects. The computation of the singular solution for a SIR epidemic system with vaccination control is performed; a constructive algorithm for the computation of a bang-singular-bang optimal solution is proposed and, for the specific choice of the parameters typical for a SIR model, the two switching instants, as well as the singular profile, are determined.

An optimal control problem formulated to reduce the infection diffusion of an epidemic disease is solved in a locally linearised context. The approach has been driven by the necessity of using a state observer, since the state variable to be controlled is not directly measurable.

We propose a novel algorithmic framework for the asynchronous and distributed optimization of multi-agent systems. We consider the constrained minimization of a nonconvex and nonsmooth partially separable sum-utility function, i.e., the cost function of each agent depends on the optimization variables of that agent and of its neighbors. This partitioned setting arises in several applications of practical interest.