Robust Discrete-Time Lateral Control of Racecars by Unknown Input Observers

This brief addresses the robust lateral control problem for self-driving racecars. It proposes a discrete-time estimation and control solution consisting of a delayed unknown input-state observer (UIO) and a robust tracking controller. Based on a nominal vehicle model, describing its motion with respect to a generic desired trajectory and requiring no information about the surrounding environment, the observer reconstructs the total force disturbance signal, resulting from imperfect knowledge of the time-varying tire-road interface characteristics, presence of other vehicles nearby, wind gusts, and other model uncertainty. Then, the controller actively compensates the estimated force and asymptotically steers the tracking error to zero. The brief also presents a closed-loop stability proof of the method, ensuring perfect asymptotic estimation and tracking by the controlled vehicle. The proposed solution advantageously needs no a-priori information about the total disturbance boundedness, additional variables to model uncertainty, or observer parameters to be tuned. Its effectiveness and superiority to existing methods are studied in theory and shown in simulations where a full racecar model, based on the vehicle dynamics blockset, is required to track aggressive maneuvers. Through a faster and more accurate disturbance estimation, the solution robustly ensures better dynamic responses even with measurement noise

What can we learn from atomistic simulations of bioactive glasses?

In the last decades, most experimental efforts have been devoted to design bioactive glasses (please consult the Editor’s note in order to clarify the usage of the terms bioglass, bioactive glass and biocompatible glasses) with enhanced biological and mechanical properties by adding specific ions to known bioactive compositions. Concurrently, computational research has been focused to the understanding of the relationships between bioactivity and composition by rationalization of the role of the doping ions. Thus, a deep knowledge of the structural organization of the constituent atoms of the bioactive glasses has been gained by the employment of ab initio and classical molecular dynamics simulations techniques. This chapter reviews the recent successes in this field by presenting, in a concise way, the structure–properties relationships of silicate, phospho-silicate and phosphate glasses with potential bioactive properties

H2O2 adsorption and dissociation on various CeO2(111) surface models: a first-principles study

Periodic density functional theory (DFT) calculations using the hybrid PBE0 functional and atom-centered Gaussian functions as basis sets were carried out to investigate the absorption and the first steps involved in the decomposition of hydrogen peroxide (H2O2) on three different models of the ceria (111) surface. One of the models is a clean surface, and the others are defective and partially hydroxylated ceria surfaces. On the clean surface, we found that the minimum energy path of hydrogen peroxide decomposition involves a three-step process, i.e., adsorption, deprotonation, and formation of the peroxide anion, stabilized through its interaction with the surface at a Ce (IV) site, with activation barriers of less than about 0.5 eV. The subsequent formation of superoxide anions and molecular oxygen species is attributed to electron transfer from the reactants to the Ce (IV) ions underneath. On the defective surface, H2O2dissociation is an energetically downhill reaction thermodynamically driven by the healing of the O vacancies, after the reduction and decomposition of H2O2into oxygen and water. On the hydroxylated surface, H2O2is first adsorbed by forming a favorable H-bond and then undergoes heterolytic dissociation, forming two hydroxyl groups at two vicinal Ce sites

Lateral Wind Estimation and Backstepping Compensation for Safer Self-Driving Racecars

This paper addresses the lateral wind gust estimation and compensation problem for racecar models. A wind-sensorless solution, i.e. a solution not using direct wind measures, is proposed. More precisely, by modeling the wind disturbance as a fully unknown input signal, an input-state observer is derived using only information about the vehicle’s longitudinal speed and lateral pose relative to the road. The observer is characterized by a simple structure, explicit closed-form, direct implementability on a micro-controller, and dead-beat property, i.e. it ensures the convergence of the estimation error in a finite time. Moreover, leveraging on the reconstructed wind data, a backstepping wind-compensation controller is also proposed, allowing asymptotic tracking of a path with desired curvature and providing the end-user with a free control parameter specifying the desired tracking speed. Formal proofs of the estimation error and tracking error convergence are given. Performance evaluation of the proposed solution is obtained in simulation by closing in the loop the full nonlinear model of a real racecar, the Robocar system, with the proposed estimation and control method. Both the estimator and the controller are shown to outperform existing solutions, even in the presence of noisy measurements

The antioxidant properties of Ce-containing bioactive glass nanoparticles explained by Molecular Dynamics simulations

Molecular dynamics simulations of two glass nanoparticles with composition 25Na2O·25CaO 50SiO2 mol% (Ce-K NP) and 46.1SiO2·24.4Na2O·26.9CaO· 2.6P2O5 mol.% (Ce-BG NP) doped with 3.6 mol% of CeO2 have been carried out in order to explain the enhanced antioxidant properties of the former glass with respect to the latter. The present models show that the different catalase mimetic activity of the two NPs is related to the Ce3+/Ce4+ ratio exposed at their surface. In fact, this ratio is about 3.5 and 13 in the bulk and at the surface of the Ce-BG NP, and 1.0 and 2.1 in the bulk and at the surface of the Ce-K NPs, respectively. Since both oxidation states are necessary for the catalysis of the dismutation reaction of hydrogen peroxides, NPs with a very high Ce3+/Ce4+ ratio possess poorer antioxidant properties. Moreover, our simulations reveal that the already low silicate connectivity found in the bulk glasses examined here is further reduced on the nanoparticle surface, whereas the Na+/Ca2+ ratio rapidly increases. Sodium, calcium and cerium sites in proximity of the surface are found to be under-coordinated, prone to quickly react with water present in physiological environments, thus accelerating the glass biodegradatio

Multiscale Molecular Dynamics Simulation of Multiple Protein Adsorption on Gold Nanoparticles

A multiscale molecular dynamics simulation study has been carried out in order to provide in-depth information on the adsorption of hemoglobin, myoglobin, and trypsin over citrate-capped AuNPs of 15 nm diameter. In particular, determinants for single proteins adsorption and simultaneous adsorption of the three types of proteins considered have been studied by Coarse-Grained and Meso-Scale molecular simulations, respectively. The results, discussed in the light of the controversial experimental data reported in the current experimental literature, have provided a detailed description of the (i) recognition process, (ii) number of proteins involved in the early stages of corona formation, (iii) protein competition for AuNP adsorption, (iv) interaction modalities between AuNP and protein binding sites, and (v) protein structural preservation and alteration

Computational Modeling of Silicate Glasses: A Quantitative Structure-Property Relationship Perspective

This article reviews the present state of Quantitative Structure-Property Relationships (QSPR) in glass design and gives an outlook into future developments. First an overview is given of the statistical methodology, with particular emphasis to the integration of QSPR with molecular dynamics simulations to derive informative structural descriptors. Then, the potentiality of this approach as a tool for interpretative and predictive purposes is highlighted by a number of recent inspiring applications

Energy refurbishment planning of Italian school buildings using data-driven predictive models

In the current practice, the design of energy refurbishment interventions for existing buildings is typically addressed by performing time-consuming software-based numerical simulations. However, this approach may be not suitable for preliminary assessment studies, especially when large building portfolios are involved. Therefore, this research work aims at developing simplified data-driven predictive models to estimate the energy consumption of existing school buildings in Italy and support the decision-making process in energy refurbishment intervention planning at a large scale. To accomplish this, an extensive database is assembled through comprehensive on-site surveys of school buildings in Southern Italy. For each school, a Building Information Modelling (BIM) model is developed and validated considering real energy consumption data. These BIM models serve in the design of suitable energy refurbishment interventions. Moreover, a comprehensive parametric investigation based on refined energy analyses is carried out to significantly improve and integrate the dataset. To derive the predictive models, firstly the most relevant parameters for energy consumption are identified by performing sensitivity analyses. Based on these findings, predictive models are generated through a multiple linear regression method. The suggested models provide an estimation of the energy consumption of the “as-built” configuration, as well as the costs and benefits of alternative energy refurbishment scenarios. The reliability of the proposed simplified relationships is substantiated through a statistical analysis of the main error indices. Results highlight that the building's shape factor (i.e., the ratio between the building's envelope area and its volume) and the area-weighted average of the thermal properties of the building envelope significantly affect both the energy consumption of school buildings and the achievable savings through retrofitting interventions. Finally, a framework for the preliminary design of energy refurbishment of buildings, based on the implementation of the herein developed predictive model, is proposed and illustrated through a worked example application. Worth noting that, while the proposed approach is currently limited to school buildings, the methodology can conceptually be extended to any building typology, provided that suitable data on energy consumption are available

Folding mechanisms steer the amyloid fibril formation propensity of highly homologous proteins

Significant advances in the understanding of the molecular determinants of fibrillogenesis can be expected from comparative studies of the aggregation propensities of proteins with highly homologous structures but different folding pathways. Here, we fully characterize, by means of stopped-flow, T-jump, CD and DSC experiments, the unfolding mechanisms of three highly homologous proteins, zinc binding Ros87 and Ml153-149 and zinc-lacking Ml452-151. The results indicate that the three proteins significantly differ in terms of stability and (un)folding mechanisms. Particularly, Ros87 and Ml153-149 appear to be much more stable to guanidine denaturation and are characterized by folding mechanisms including the presence of an intermediate. On the other hand, metal lacking Ml452-151 folds according to a classic two-state model. Successively, we have monitored the capabilities of Ros87, Ml452-151 and Ml153-149 to form amyloid fibrils under native conditions. Particularly, we show, by CD, fluorescence, DLS, TEM and SEM experiments, that after 168 hours, amyloid formation of Ros87 has started, while Ml153-149 has formed only amorphous aggregates and Ml452-151 is still monomeric in solution. This study shows how metal binding can influence protein folding pathways and thereby control conformational accessibility to aggregation-prone states, which in turn changes aggregation kinetics, shedding light on the role of metal ions in the development of protein deposition diseases