arXiv subjects

Thomas Vasileiou

Publications and source records attributed to Thomas Vasileiou.

3 recordsLinked to original sources

arXiv2020

Model-based resonance tracking of linear systems

The present paper develops recursive algorithms to track shifts in the resonance frequency of linear systems in real time. To date, automatic resonance tracking has been limited to non-model-based approaches, which rely solely on the phase difference between a specific input and output of the system. Instead, we propose a transformation of the system into a complex-valued representation, which allows us to abstract the resonance shifts as an exogenous disturbance acting on the excitation frequency, perturbing the excitation frequency from the natural frequency of the plant. We then discuss the resonance tracking task in two parts: recursively identifying the frequency disturbance and incorporating an update of the excitation frequency in the algorithm. The complex representation of the system simplifies the design of resonance tracking algorithms due to the applicability of well-established techniques. We discuss the stability of the proposed scheme, even in cases that seriously challenge current phase-based approaches, such as nonmonotonic phase differences and multiple-input multiple-output systems. Numerical simulations further demonstrate the performance of the proposed resonance tracking scheme.

math.OC↗

arXiv2019

On the stability analysis of deep neural network representations of an optimal state-feedback

Recent work have shown how the optimal state-feedback, obtained as the solution to the Hamilton-Jacobi-Bellman equations, can be approximated for several nonlinear, deterministic systems by deep neural networks. When imitation (supervised) learning is used to train the neural network on optimal state-action pairs, for instance as derived by applying Pontryagin's theory of optimal processes, the resulting model is referred here as the guidance and control network. In this work, we analyze the stability of nonlinear and deterministic systems controlled by such networks. We then propose a method utilising differential algebraic techniques and high-order Taylor maps to gain information on the stability of the neurocontrolled state trajectories. We exemplify the proposed methods in the case of the two-dimensional dynamics of a quadcopter controlled to reach the origin and we study how different architectures of the guidance and control network affect the stability of the target equilibrium point and the stability margins to time delay. Moreover, we show how to study the robustness to initial conditions of a nominal trajectory, using a Taylor representation of the neurocontrolled neighbouring trajectories.

cs.NE↗

arXiv2018

Imparting icephobicity with substrate flexibility

Ice accumulation hinders the performance of, and poses safety threats for infrastructure both on the ground and in the air. Previously, rationally designed superhydrophobic surfaces have demonstrated some potential as a passive means to mitigate ice accretion; however, further studies on material solutions that reduce impalement and contact time for impacting supercooled droplets and can also repel droplets that freeze during surface contact are urgently needed. Here we demonstrate the collaborative effect of substrate flexibility and surface nanotexture on enhancing both icephobicity and the repellency of viscous droplets. We first investigate the influence of increased viscosity on impalement resistance and droplet-substrate contact time after impact. Then we examine the effect of droplet partial solidification on recoil and simulate more challenging icing conditions by impacting supercooled water droplets onto flexible and rigid surfaces containing ice nucleation promoters. We demonstrate a passive mechanism for shedding partially solidified droplets under conditions where partial solidification occurs much faster than the natural droplet oscillation which does not rely on converting droplet surface energy into kinetic energy. Using an energy-based model, we identify a previously unexplored mechanism whereby the substrate oscillation and velocity govern the rebound process, with low-areal density and moderately stiff substrates acting to efficiently absorb the incoming droplet kinetic energy and rectify it back, allowing droplets to overcome adhesion and gravitational forces, and recoil. This mechanism applies for a range of droplet viscosities, spanning from low to high viscosity fluids and even ice slurries, which do not rebound from rigid superhydrophobic substrates.

cond-mat.soft↗