The paper deals with the design of an improved model predictive control scheme for achieving station-keeping of spacecrafts in a quasi Halo orbit around the $L_2$ point in the Earth-Moon system. The improvement is obtained thanks to a multi-rate sampled-data trajectory planner that allows for...
01a Articolo in rivista
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In this letter, we propose a new sampled-data controller for stabilization of the attitude dynamics at a desired constant configuration. The design is based on discrete-time interconnection and damping assignment (IDA) passivity-based control (PBC) and the recently proposed Hamiltonian...
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In this paper, new results for passivation and stabilization of discrete-time nonlinear systems via energy balancing are established. When specified on sampled-data systems, the approach is constructive for computing stabilizing digital controllers that assign, at all sampling instants, a target...
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In this paper, on the basis of a recently proposed discrete-time port-Hamiltonian representation of sampled-data dynamics, we propose a new time-varying digital feedback for steering mobile robots. The quality of the proposed passivity-based control is validated and compared through simulations...
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In this paper, the behavior of scalar multi-agent systems over networks subject to time-driven jumps. Assuming that all agents communicate through distinct communication digraphs at jump and flow times, the asymptotic multi-consensus behavior of the hybrid network is explicitly characterized. The...
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This paper presents a control strategy aimed at efficiently operating a service area equipped with stations for plug-in electric vehicles’ fast charging, renewable energy sources, and an electric energy storage unit. The control requirements here considered are in line with the perspective of a...
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The use and creation of machine‐learning‐based solutions to solve problems or reduce their computational costs are becoming increasingly widespread in many domains. Deep Learning plays a large part in this growth. However, it has drawbacks such as a lack of explainability and behaving as a black‐...
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Machine learning techniques are a driving force for research in various fields, from credit card fraud detection to stock analysis. Recently, a growing interest in increasing human involvement has emerged, with the primary goal of improving the interpretability of machine learning models. Among...
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Symbolic execution is a program analysis technique that aims to automatically identify interesting inputs for an application, using them to generate program executions covering different parts of the code. It is widely used in the context of vulnerability discovery and reverse engineering. In this...
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In this paper we consider the estimation problem for linear stochastic systems affected by multiple known and time-varying delays on all the output signals. Based on a modification of a previous proposal we prove for the first time the result that a filter based on simple eigenvalue assignment of...