Ford Motor Company (Czechia)

Abstract We consider the problem of enforcing safety in multi‐input systems with distinct input delays via the use of Control Barrier Functions (CBFs). For systems with input delay(s), a popular approach for enforcing safety is the combination of a CBF designed for the delay‐free system with state‐predictors that compensate the input delays. Typically, this comes with the assumption that the system does not violate safety constraints before all input delays have been compensated and the system is fully under control. In this paper, we introduce two control approaches that enforce safety before all input delays have been compensated, whenever it is possible to do so. We do this by utilizing a robust CBF formulation that treats longer‐delayed inputs as known disturbances when determining control effort for shorter delayed inputs. This formulation ensures that, whenever possible, a subset of input channels with shorter delays will be utilized for keeping the system in the admissible safe set before longer input delays have been compensated. The effectiveness of our approaches is demonstrated with two numerical examples.

There has been significant progress in sensing, perception, and localization for automated driving, However, due to the wide spectrum of traffic/road structure scenarios and the long tail distribution of human driver behavior, it has remained an open challenge for an intelligent vehicle to always know how to make and execute the best decision on road given available sensing / perception / localization information. In this chapter, we talk about how artificial intelligence and more specifically, reinforcement learning, can take advantage of operational knowledge and safety reflex to make strategical and tactical decisions. We discuss some challenging problems related to the robustness of reinforcement learning solutions and their implications to the practical design of driving strategies for autonomous vehicles. We focus on automated driving on highway and the integration of reinforcement learning, vehicle motion control, and control barrier function, leading to a robust AI driving strategy that can learn and adapt safely.

This paper introduces a parameter adaptation-based control law for a class of nonlinear, control-affine, safety-critical systems subject to additive, parameter-affine model uncertainty. It is shown that the uncertainty is learned in fixed-time, i.e., within a finite time independent of the initial parameter estimates, without requiring persistence of excitation or rank conditions on the associated regressor matrix. A monotonically decreasing bound on the error of the estimated uncertainty is derived and used in the proposed control barrier function-based control law for guaranteed safety of the system trajectories. It is then proven that the parameter adaptation law is robust to bounded measurement noise and non-parametric perturbations to the system in that the estimated uncertainty still converges to a known neighborhood of the true uncertainty in fixed-time. The advantages of the approach are highlighted in a comparative numerical study, and the method is validated on a quadrotor trajectory-tracking problem subject to safety requirements and an unknown wind field.

Inhabitants of urban areas such as Mexico City are exposed to various anthropogenic pollutants. Among them, atmospheric particles and volatile organic compounds (VOC), which usually exceed the limits established by current regulations. Air pollutants are of great interest because of their important role in atmospheric chemistry, the complexity of their sources, and their adverse effects on health. PM2.5 are fine particles that serve as indicators of poor air quality, such black carbon, which also contributes to global warming. Both cause serious damage to the respiratory system, mainly in children and the elderly. Carbonyl compounds are members of the VOC family, they are precursors of tropospheric ozone and are considered dangerous compounds, some of them are already classified as carcinogens. Air pollution is studied in two environments; extramural and intramural, due to the time we spend indoors (home, office, classrooms and other workplaces), which is approximately 90%. The problem of indoor pollution is caused by various factors, including the number and variety of emission sources. In this thesis, the concentration levels PM2.5, carbon species and carbonyls were evaluated in intramural and extramural environments in Mexico City. For the evaluation, three sampling campaigns for PM2.5 and carbonyls in the air were designed and carried out considering the spring, summer and autumn seasons in intramural and extramural environments. Each campaign was carried out in three areas of Mexico City: North in a house near the facilities of the UAM-Azcapotzalco, Center in the University of the Americas on Av. Insurgentes and South in the Center for Atmospheric Sciences in Coyoacán. Outdoor sampling was carried out on the buildings’ roofs of two campuses and a residence, while the indoor sampling was carried out in office areas and residence. The experimental data was carried out through the gravimetric analysis of quartz and Teflon filters, the use of thermo-optical equipment and HPLC UV-vis chromatography. As a result of this project, it was found that the concentrations of particles were higher outside the walls than inside the walls, mainly in the spring time compared to those obtained in the summer and fall of 2019. The content of carbonaceous species in PM2.5 was around 20%nof which 10% turned out to be elemental carbon and 90% organic carbon, in which PAHs can be included among other organic species. Total carbon concentrations were higher intramural than extramural. Total carbon concentrations were higher intramural than extramural. The indoor-outdoor (I / E) ratio for PM2.5 indicated that outdoor sources predominate over indoor sources. On the other hand, the average concentrations of carbonyls in the three sites were higher inside the buildings than outside, especially in the summer season. Acetone concentrations had many variations in all samplings, reaching values close to 1000 μg m-3 in the intramural area. The carbonyls with the highest concentration values were acetaldehyde and formaldehyde. Acrolein also presented some variations in its concentration, especially in summer.Propionaldehyde and butyraldehyde presented low concentrations in all samplings. The I/E ratio was evaluated for each carbonyl, which showed values greater than 1 in most cases, which confirms an important contribution from intramural sources such as the use of cleaning products, the material of the building and of furniture and human activities that could be a source of emission of carbonyl compounds.

Abstract To realize robust structural design, the effects of the adhesive fillet, overlap length and unbonded area in adhesive‐bonded joints need to be fully understood and incorporated into a fatigue life estimation method. In the present work, both static and fatigue experiments are performed on six types of adhesive‐bonded joints to illuminate these effects systematically. A straightforward total fatigue life evaluation method is proposed to address these effects. A statistical crack initiation model is established based on the fatigue data of bulk adhesive specimens. Growth life is calculated using the interfacial crack model and mixed mode crack growth method. Good correlation is observed between the calculated and experimental fatigue lives. Furthermore, the effects of the adhesive fillet, overlap length and unbonded area are analysed based on both calculated and experimental results. Results indicate that adhesive fillet postpones crack initiation by reducing local strain level, both overlap length and unbonded area change the growth life by length. Besides, overlap length promotes the fraction of mode II strain energy release rate in total, reducing crack growth rates and extending growth life.

Abstract Additive manufacturing, i.e., 3D printing, is being increasingly utilized to fabricate a variety of complex‐shaped electronics and energy devices (e.g., batteries, supercapacitors, and solar cells) due to its excellent process flexibility, good geometry controllability, as well as cost and material waste reduction. In this review, the recent advances in 3D printing of emerging batteries are emphasized and discussed. The recent progress in fabricating 3D‐printed batteries through the major 3D‐printing methods, including lithography‐based 3D printing, template‐assisted electrodeposition‐based 3D printing, inkjet printing, direct ink writing, fused deposition modeling, and aerosol jet printing, are first summarized. Then, the significant achievements made in the development and printing of battery electrodes and electrolytes are highlighted. Finally, major challenges are discussed and potential research frontiers in developing 3D‐printed batteries are proposed. It is expected that with the continuous development of printing techniques and materials, 3D‐printed batteries with long‐term durability, favorable safety as well as high energy and power density will eventually be widely used in many fields.