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This article presents the new theoretical concept of environing media, which is developed to offer critical insight into how processes of mediation affect how we perceive of, manage and use the environment. Building on the insight that the environment has been in a continuous slow process of change that is now escalating due to human impacts, the article sketches a history of how environmental change and mediation are intertwined. Taking the history of agriculture as a case for the theoretical development, it shows how the current digitization of farming and implementation of AI systems in precision agriculture is the last of a long series in which environmental mediation come to play a crucial role in the forging of human–Earth relations. The article thereby shows the complex interplay between knowing and changing the environment as media technologies produce new epistemologies that in turn produce new interventions. QC 20221227

In this paper, we show how Behavior Trees that have performance guarantees, in terms of safety and goal convergence, can be extended with components that were designed using machine learning, without destroying those performance guarantees. Machine learning approaches such as reinforcement learning or learning from demonstration can be very appealing to AI designers that want efficient and realistic behaviors in their agents. However, those algorithms seldom provide guarantees for solving the given task in all different situations while keeping the agent safe. Instead, such guarantees are often easier to find for manually designed model-based approaches. In this paper we exploit the modularity of behavior trees to extend a given design with an efficient, but possibly unreliable, machine learning component in a way that preserves the guarantees. The approach is illustrated with an inverted pendulum example. Comment: Accepted as Regular Paper to The 61th IEEE Conference on Decision and Control (CDC 2022)

For a class of spatio-temporal tasks defined by a fragment of Signal Temporal Logic (STL), we construct a nonsmooth time-varying control barrier function (CBF) and develop a controller based on a set of simple optimization problems. Each of the optimization problems invokes constraints that allow to exploit the piece-wise smoothness of the CBF for optimization additionally to the common gradient constraint in the context of CBFs. In this way, the conservativeness of the control approach is reduced in those points where the CBF is nonsmooth. Thereby, nonsmooth CBFs become applicable to time-varying control tasks. Moreover, we overcome the problem of vanishing gradients for the considered class of constraints which allows us to consider more complex tasks including disjunctions compared to approaches based on smooth CBFs. As a well-established and systematic method to encode spatiotemporal constraints, we define the class of tasks under consideration as an STL-fragment. The results are demonstrated in a relevant simulation example. Comment: accepted for presentation at the 61st IEEE Conference on Decision and Control 2022

AbstractMagnetopolymers are of interest in smart material applications; however, changing their magnetic properties post synthesis is complicated. In this study, we introduce easily programmable polymer magnetic composites comprising 2D lattices of droplets of solid-liquid phase change material, with each droplet containing a single magnetic dipole particle. These composites are ferromagnetic with a Curie temperature defined by the rotational freedom of the particles above the droplet melting point. We demonstrate magnetopolymers combining high remanence characteristics with Curie temperatures below the composite degradation temperature. We easily reprogram the material between four states: (1) a superparamagnetic state above the melting point which, in the absence of an external magnetic field, spontaneously collapses to; (2) an artificial spin ice state, which after cooling forms either; (3) a spin glass state with low bulk remanence, or; (4) a ferromagnetic state with high bulk remanence when cooled in the presence of an external magnetic field. We observe the spontaneous emergence of 2D magnetic vortices in the spin ice and elucidate the correlation of these vortex structures with the external bulk remanence. We also demonstrate the easy programming of magnetically latching structures.

In this paper, we present a distributed model predictive control (DMPC) scheme for dynamically decoupled systems which are subject to state constraints, coupling state constraints and input constraints. In the proposed control scheme, neighbor-to-neighbor communication suffices and all subsystems solve their local optimization problem in parallel. The approach relies on consistency constraints which define a neighborhood around each subsystem's reference trajectory where the state of the respective subsystem is guaranteed to stay in. Reference trajectories and consistency constraints are known to neighboring subsystems. Contrary to other relevant approaches, the reference trajectories are improved iteratively. Besides, the presented approach allows the formulation of convex optimization problems even in the presence of non-convex state constraints. The algorithm's effectiveness is demonstrated with a simulation. Comment: accepted for presentation at the 61st IEEE Conference on Decision and Control 2022

We propose a control protocol based on the prescribed performance control (PPC) methodology for a quadrotor unmanned aerial vehicle (UAV). Quadrotor systems belong to the class of underactuated systems for which the original PPC methodology cannot be directly applied. We introduce the necessary design modifications to stabilize the considered system with prescribed performance. The proposed control protocol does not use any information of dynamic model parameters or exogenous disturbances. Furthermore, the stability analysis guarantees that the tracking errors remain inside of designer-specified time-varying functions, achieving prescribed performance independent from the control gains' selection. Finally, simulation results verify the theoretical results. Comment: 8 pages, 6 figures, to appear at 61st IEEE Conference on Decision and Control 2022

Kilamba, the first of the new centralities in Angola, is increasingly visible in recent urban scholarship about Luanda, further establishing it as the symbol of both this “new” post-war city and the “New Angola.” Within local discourses of progress, its emergence from within “petro-urbanism,” and its size and modern aesthetics are emphasized, while little attention has been directed towards understanding the actual contributions of its workers, particularly the women who spend a significant part of their day cleaning Kilamba’s apartments. In this paper, we combine a social reproduction framework with infrastructure studies to trace the labor of Kilamba’s female domestic workers, in order to demonstrate how their everyday practices uphold the status and materiality of this centrality, even as their work is invisibilized. In doing so, we understand their commentaries about this space, often refracted through descriptions of their homes, as critiques of the infrastructural priorities of the “New Angola.” QC 20230227 Grounding and Worlding Urban Infrastructure (GROWL)

AbstractIn this paper, we solve nonlinear conservation laws using the radial basis function generated finite difference (RBF-FD) method. Nonlinear conservation laws have solutions that entail strong discontinuities and shocks, which give rise to numerical instabilities when the solution is approximated by a numerical method. We introduce a residual-based artificial viscosity (RV) stabilization framework adjusted to the RBF-FD method, where the residual of the conservation law adaptively locates discontinuities and shocks. The RV stabilization framework is applied to the collocation RBF-FD method and the oversampled RBF-FD method. Computational tests confirm that the stabilized methods are reliable and accurate in solving scalar conservation laws and conservation law systems such as compressible Euler equations.