A Convex Feasibility Approach to Anytime Model Predictive Control

This paper proposes to decouple performance optimization and enforcement of asymptotic convergence in Model Predictive Control (MPC) so that convergence to a given terminal set is achieved independently of how much performance is optimized at each sampling step. By embedding an explicit decreasing condition in the MPC constraints and thanks to a novel and very easy-to-implement convex feasibility solver proposed in the paper, it is possible to run an outer performance optimization algorithm on top of the feasibility solver and optimize for an amount of time that depends on the available CPU resources within the current sampling step (possibly going open-loop at a given sampling step in the extreme case no resources are available) and still guarantee convergence to the terminal set. While the MPC setup and the solver proposed in the paper can deal with quite general classes of functions, we highlight the synthesis method and show numerical results in case of linear MPC and ellipsoidal and polyhedral terminal sets.Comment: 8 page

Energy-aware robust model predictive control based on noisy wireless sensors

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Stabilizing Model Predictive Control of Stochastic Constrained Linear Systems

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On the Virtualization of Audio Transducers

In audio transduction applications, virtualization can be defined as the task of digitally altering the acoustic behavior of an audio sensor or actuator with the aim of mimicking that of a target transducer. Recently, a digital signal preprocessing method for the virtualization of loudspeakers based on inverse equivalent circuit modeling has been proposed. The method applies Leuciuc’s inversion theorem to obtain the inverse circuital model of the physical actuator, which is then exploited to impose a target behavior through the so called Direct–Inverse–Direct Chain. The inverse model is designed by properly augmenting the direct model with a theoretical two-port circuit element called nullor. Drawing on this promising results, in this manuscript, we aim at describing the virtualization task in a broader sense, including both actuator and sensor virtualizations. We provide ready-to-use schemes and block diagrams which apply to all the possible combinations of input and output variables. We then analyze and formalize different versions of the Direct–Inverse–Direct Chain describing how the method changes when applied to sensors and actuators. Finally, we provide examples of applications considering the virtualization of a capacitive microphone and a nonlinear compression driver

Hydration Structures on γ-Alumina Surfaces With and Without Electrolytes Probed by Atomistic Molecular Dynamics Simulations

A wide range of systems, both engineered and natural, feature aqueous electrolyte solutions at interfaces. In this study, the structure and dynamics of water at the two prevalent crystallographic terminations of gamma-alumina, [110] and [100], and the influence of salts─sodium chloride, ammonium acetate, barium acetate, and barium nitrate on such properties─were investigated using equilibrium molecular dynamics simulations. The resulting interfacial phenomena were quantified from simulation trajectories via atomic density profiles, angle probability distributions, residence times, 2-D density distributions within the hydration layers, and hydrogen bond density profiles. Analysis and interpretation of the results are supported by simulation snapshots. Taken together, our results show stronger interaction and closer association of water with the [110] surface, compared to [100], while ion-induced disruption of interfacial water structure was more prevalent at the [100] surface. For the latter, a stronger association of cations is observed, namely sodium and ammonium, and ion adsorption appears determined by their size. The differences in surface-water interactions between the two terminations are linked to their respective surface features and distributions of surface groups, with atomistic-scale roughness of the [110] surface promoting closer association of interfacial water. The results highlight the fundamental role of surface characteristics in determining surface-water interactions, and the resulting effects on ion-surface and ion-water interactions. Since the two terminations of gamma-alumina considered represent interfaces of significance to numerous industrial applications, the results provide insights relevant for catalyst preparation and adsorption-based water treatment, among other applications

Interactions between γ-alumina surfaces in water and aqueous salt solutions

Particle agglomeration is relevant to numerous industrial applications and consumer products. The present work explores interactions between and agglomeration of gamma (γ)-alumina nanoparticles in pure water and dilute aqueous salt solutions. To characterize surface- and salt-specific effects, potential of mean force (PMF) profiles between γ-alumina surfaces ([110] and [100] facets) are extracted using classical molecular dynamics (MD) simulations. Supporting experiments are conducted using dynamic light scattering (DLS) to investigate agglomeration at the macroscale. The ion pairs considered are sodium chloride, ammonium acetate, barium nitrate, and barium acetate; sampling a broad range of the Hofmeister series. As particle surfaces approach contact, free-energy fluctuations of the PMF profiles reflect structural adjustments of the intervening aqueous phase. We extract values for the cohesive energy from the MD results, and parse the resultant effective pair interactions into van der Waals and electrostatic contributions. Molecular scale findings from simulations correlate with hydrodynamic radii of γ-alumina nanoparticles, obtained from DLS experiments. The results highlight the applicability of molecular simulations to identify the origins of macroscale observables