Precision Attitude Stabilization with Intermittent External Torque

The attitude stabilization of a micro-satellite employing a variable-amplitude cold gas thruster which reflects as a time varying gain on the control input is considered. Existing literature uses a persistence filter based approach that typically leads to large control gains and torque inputs during specific time intervals corresponding to the 'on' phase of the external actuation. This work aims at reducing the transient spikes placed upon the torque commands by the judicious introduction of an additional time varying scaling signal as part of the control law. The time update mechanism for the new scaling factor and overall closed-loop stability are established through a Lyapunov-like analysis. Numerical simulations highlight the various features of this new control algorithm for spacecraft attitude stabilization subject to torque intermittence.Comment: Presented as paper AAS 21-402 at the 31st AAS/AIAA Space Flight Mechanics Meeting, Virtual, February 1-4 202

Consensus and Flocking under Communication Failures for a Class of Cucker-Smale Systems

In this paper, we study sufficient conditions for the emergence of asymptotic consensus and flocking in a certain class of non-linear generalised Cucker-Smale systems subject to multiplicative communication failures. Our approach is based on the combination of strict Lyapunov design together with the formulation of a suitable persistence condition for multi-agent systems. The latter can be interpreted as a lower bound on the algebraic connectivity of the time-average of the interaction graph generated by the communication weights, and provides quantitative decay estimates for the variance functional along the solutions of the system

Unsupervised clustering of IoT signals through feature extraction and self organizing maps

This thesis scope is to build a clustering model to inspect the structural properties of a dataset composed of IoT signals and to classify these through unsupervised clustering algorithms. To this end, a feature-based representation of the signals is used. Different feature selection algorithms are then used to obtain reduced feature spaces, so as to decrease the computational cost and the memory demand. Thus, the IoT signals are clustered using Self-Organizing Maps (SOM) and then evaluatedope