Investigation, development and application of optimal output feedback theory. Volume 2: Development of an optimal, limited state feedback outer-loop digital flight control system for 3-D terminal area operation

This report contains the development of a digital outer-loop three dimensional radio navigation (3-D RNAV) flight control system for a small commercial jet transport. The outer-loop control system is designed using optimal stochastic limited state feedback techniques. Options investigated using the optimal limited state feedback approach include integrated versus hierarchical control loop designs, 20 samples per second versus 5 samples per second outer-loop operation and alternative Type 1 integration command errors. Command generator tracking techniques used in the digital control design enable the jet transport to automatically track arbitrary curved flight paths generated by waypoints. The performance of the design is demonstrated using detailed nonlinear aircraft simulations in the terminal area, frequency domain multi-input sigma plots, frequency domain single-input Bode plots and closed-loop poles. The response of the system to a severe wind shear during a landing approach is also presented

Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport

Hierarchical abstractions are a methodology for solving large-scale graph problems in various disciplines. Coarsening is one such approach: it generates a pyramid of graphs whereby the one in the next level is a structural summary of the prior one. With a long history in scientific computing, many coarsening strategies were developed based on mathematically driven heuristics. Recently, resurgent interests exist in deep learning to design hierarchical methods learnable through differentiable parameterization. These approaches are paired with downstream tasks for supervised learning. In practice, however, supervised signals (e.g., labels) are scarce and are often laborious to obtain. In this work, we propose an unsupervised approach, coined OTCoarsening, with the use of optimal transport. Both the coarsening matrix and the transport cost matrix are parameterized, so that an optimal coarsening strategy can be learned and tailored for a given set of graphs. We demonstrate that the proposed approach produces meaningful coarse graphs and yields competitive performance compared with supervised methods for graph classification and regression.Comment: AAAI 2021. Code is available at https://github.com/matenure/OTCoarsenin

Hierarchical Optimal Transport for Unsupervised Domain Adaptation

In this paper, we propose a novel approach for unsupervised domain adaptation, that relates notions of optimal transport, learning probability measures and unsupervised learning. The proposed approach, HOT-DA, is based on a hierarchical formulation of optimal transport, that leverages beyond the geometrical information captured by the ground metric, richer structural information in the source and target domains. The additional information in the labeled source domain is formed instinctively by grouping samples into structures according to their class labels. While exploring hidden structures in the unlabeled target domain is reduced to the problem of learning probability measures through Wasserstein barycenter, which we prove to be equivalent to spectral clustering. Experiments on a toy dataset with controllable complexity and two challenging visual adaptation datasets show the superiority of the proposed approach over the state-of-the-art

Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots

We show dynamic locomotion strategies for wheeled quadrupedal robots, which combine the advantages of both walking and driving. The developed optimization framework tightly integrates the additional degrees of freedom introduced by the wheels. Our approach relies on a zero-moment point based motion optimization which continuously updates reference trajectories. The reference motions are tracked by a hierarchical whole-body controller which computes optimal generalized accelerations and contact forces by solving a sequence of prioritized tasks including the nonholonomic rolling constraints. Our approach has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled including the non-steerable wheels attached to its legs. We conducted experiments on flat and inclined terrains as well as over steps, whereby we show that integrating the wheels into the motion control and planning framework results in intuitive motion trajectories, which enable more robust and dynamic locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4 m/s and a reduction of the cost of transport by 83 % we prove the superiority of wheeled-legged robots compared to their legged counterparts.Comment: IEEE Robotics and Automation Letter

Synthesis and catalytic properties of hierarchically structured zeolite catalysts with intracrystalline macropores

Zeolites belong to the most important heterogeneous catalysts. They are widely applied in crude oil refining, petrochemistry, fine chemistry, as well as in environmental applications. A unique feature of zeolites is their well-ordered micropore system with pore diameters similar to the dimensions of molecules. These small pores give rise to the shape selective properties of zeolite catalysts. However, the diffusion of molecules to and from the active sites confined within the micropores is very slow, which often leads to diffusion limitations. These diffusion limitations result in reduced utilization of the zeolite crystal and can also lead to reduced selectivity or lifetime of zeolite catalysts. A nature-inspired approach to overcome such diffusion restrictions is the utilization of catalysts with an optimally designed, hierarchical structure. In nature, mass transport systems, such as trees or lungs, possess an optimized hierarchical architecture to reduce transport limitations across a wide range of length scales.1 Adapting this approach to zeolites can be realized by including at least one additional system of larger pores interconnected to the zeolitic micropores. Hereby, hierarchical zeolites coul already demonstrate enhanced diffusion properties and, consequently, better catalytic performance.3 In order to prepare a truly nature inspired catalyst, a guided material design is crucial. Therefore, the transport pore system must exhibit an optimal porosity and the zeolitic domains in between the transport pores need to be small enough to eliminate local diffusion limitations. The pore size can be neglected, if it is larger than a certain minimum pore size, usually in the range of macropores or very large mesopores.3 However, preparation approaches for hierarchical zeolites are often unguided and result mostly in materials containing relative small mesopores. In this contribution we introduce a synthesis approach for zeolite single crystals with intracrystalline macropores by a so-called inverse crystallization, which allows control over the porosity, pore size and wall thickness of the hierarchical zeolite (see Figure 1 b). This synthesis approach utilizes mesoporous spherical silica particles as a sacrificial template for the macropore formation during zeolite synthesis by steam-assisted crystallization. Furthermore, we show the effect of these additional intracrystalline macropores on the catalytic performance for the direct conversion of methanol to short chain olefins (MTO), with focus on coke formation and catalyst lifetime. Please click Additional Files below to see the full abstract

Methodology for an integrated modelling of macro and microscopic processes in urban transport demand

The paper presents the theoretical formulation and the underlying assumptions for an activity-based approach of transport demand modelling. Starting with the definition of a time hierarchy of decision-making in the urban environment, rules are formulated that dictate the general hierarchic structure of individuals’ choices in the urban system. The temporal scale defines decisions for activities and their daily sequence, the geographical scale decisions associated to destination choice processes. We build activity plans (number and daily sequence of activities) from an empirical data set and calculate trip paths (time-spatial trajectories including transport modes and travel destinations) assuming consumers to maximize their utility in the decision-making process. First results of the translation of the theoretical model into a real-world application are shown for the city of Santiago, Chile