We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, on a global scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy, Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We also assess the reusability of some representative open-data sources.

In this paper, a sampling-based Stochastic Model Predictive Control algorithm is proposed for discrete-time linear systems subject to both parametric uncertainties and additive disturbances. One of the main drivers for the development of the proposed control strategy is the need of reliable and robust guidance and control strategies for automated rendezvous and proximity operations between spacecraft. To this end, the proposed control algorithm is validated on a floating spacecraft experimental testbed, proving that this solution is effectively implementable in real-time. Parametric uncertainties due to the mass variations during operations, linearization errors, and disturbances due to external space environment are simultaneously considered.The approach enables to suitably tighten the constraints to guarantee robust recursive feasibility when bounds on the uncertain variables are provided. Moreover, the offline sampling approach in the control design phase shifts all the intensive computations to the offline phase, thus greatly reducing the online computational cost, which usually constitutes the main limit for the adoption of Stochastic Model Predictive Control schemes, especially for low-cost on-board hardware. Numerical simulations and experiments show that the approach provides probabilistic guarantees on the success of the mission, even in rather uncertain and noise situations, while improving the spacecraft performance in terms of fuel consumption.

No abstract

In this paper, the problem of identifying inertial characteristics of a generic space vehicle relying on the physical and structural insights of the dynamical system is presented. To this aim, we exploit a recently introduced framework for the identification of physical parameters directly feeding the measurements into a backpropagation-like learning algorithm. In particular, this paper extends this approach by introducing a recursive algorithm that combines physics-based and blackbox techniques to enhance accuracy and reliability in estimating spacecraft inertia. We demonstrate through numerical results that, relying on the derived algorithm to identify the inertia tensor of a nanosatellite, we can achieve improved estimation accuracy and robustness, by integrating physical constraints and leveraging partial knowledge of the system dynamics. In particular, we show how it is possible to enhance the convergence of the physics-based algorithm to the true values by either overparametrization or introducing a black-box term that captures the unmodelled dynamics related to the offdiagonal components.