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Efficiently finding covariate model structures that minimize the need for random effects to describe pharmacological data is challenging. The standard approach focuses on identification of relevant covariates, and present methodology lacks tools for automatic identification of covariate model structures. Although neural networks could potentially be used to approximate covariate-parameter relation
We consider distributed consensus in networks where the agents have integrator dynamics of order two or higher (n≥2). We assume all feedback to be localized in the sense that each agent has a bounded number of neighbors and consider a scaling of the network through the addition of agents in a modular manner, i.e., without re-tuning controller gains upon addition. We show that standard consensus al
Shape control strategies seek to bring deformable objects towards a desired target shape. However, conventional methods focus on reaching the target shape without considering the extent to which the object is deformed during the control process. Control actions may generate unnecessary deformations and thus, increase the possibility of object over-stressing and failure. In this letter, we tackle t
Closed-loop motion planning is suitable for obstacle avoidance in dynamically changing environments due to its reactive nature, and various methods have been presented to provide (almost) global convergence. A common assumption in the control design is that the robot operates in a disjoint star world, i.e., all obstacles are strictly starshaped and mutually disjoint. However, in real-life scenario
Control systems are ubiquitous and often at the core of Cyber-Physical Systems, like cars and aeroplanes. They are implemented as embedded software, that interacts in closed loop with the physical world through sensors and actuators. As a consequence, the software cannot just be tested in isolation. To close the loop in a testing environment and root causing failure generated by different parts of
We investigated whether sign-naïve learners can infer and learn the meaning of signs after minimal exposure to continuous, naturalistic input in the form of a weather forecast in Swedish Sign Language. Participants were L1-English adults. Two experimental groups watched the forecast once (N=40) or twice (N=42); a control group did not (N=42). Participants were then asked to assign meaning to 22 ta
Aim: The study was designed to document the incidence of non-immune hydrops fetalis (NIHF) at birth and characterise associated outcomes and obstetric complications. Methods: Data on more than 1.9 million births were extracted from the Swedish Birth Register for 1997-2015. Pregnancies not affected by NIHF served as controls. National registers on mortality and hospitalisations provided follow-up i
In this chapter, we explore mathematical tools for assessing the stability, convergence, and boundedness of trajectories in generally nonlinear dynamical systems. We delve into the seminal theorems introduced by A. Lyapunov which are primarily concerned with the stability of equilibrium points. The chapter progresses to discuss the extension of Lyapunov's Theory, due to LaSalle, aimed at assessing
This paper presents a decentralized strategy for a team of N robotic manipulators cooperatively grasping and manipulating an object. A two-step strategy has been designed. In the first step, each robot runs N − 1 consensus-based estimators to estimates the wrenches applied to the object by its teammates even when direct all-to-all communication is unavailable. In the second step, each manipulator
Resource provisioning for applications hosted in the cloud is a difficult task due to inherent performance variability in the infrastructure. Control theory has proven to be an efficient tool to increase the predictability of cloud applications. However, a prerequisite for a successful control design is an adequate model of the involved dynamics. In this paper we focus on modeling of controlled se
Structure-from-Motion (SfM) has become a ubiquitous tool for camera calibration and scene reconstruction with many downstream applications in computer vision and beyond. While the state-of-the-art SfM pipelines have reached a high level of maturity in well-textured and well-configured scenes over the last decades, they still fall short of robustly solving the SfM problem in challenging scenarios.
Digital control systems introduce unavoidable computational latencies. For some controllers this time delay inhibits practical use, even though they in theory could provide more efficient control. For example, solving an optimization problem each sampling period when using model predictive control. By sampling faster than the computation time and executing independent controllers on distributed ha
Chains of tasks are ubiquitous and used in a broad spectrum of applications. In these chains, tasks execute according to their timing. Then, they communicate by writing to and reading from shared memory. The schedule of tasks and the read/write instants are naturally subject to uncertainties (variability in the execution time, interference due to shared resources of higher priority tasks, etc.). D
Modern real-time control systems can sporadically exceed the computation deadlines, which may lead to a deterioration in performance or even instability if not actively accounted for. This letter proposes a stochastic model predictive control approach that incorporates deadline miss probabilities of subsequent control task executions in a scenario tree. To account for the effect of missed deadline
This is the Replicated Computational Results (RCR) Report for the article ‘Stress Testing Control Loops in Cyber-Physical Systems’. The article proposes a novel approach for testing Cyber-Physical Systems (CPS) based on the integration of the guarantees that can be provided with the control theoretical models into the software testing practices. This RCR report describes how to reproduce the empir
Dynamic task offloading is essential for real-time robotic applications, enabling them to adapt to fluctuating computational demands and maintain efficiency under changing conditions. This paper introduces a dynamic task offloading framework that incorporates monitoring, decision making, offloading triggering, and performance monitoring to optimize resource usage by offloading real-time tasks to e
The need for information about climate change is great. This information is usually based on climate model data, which often have systematic biases. Furthermore, climate information is based on ensembles of climate models, which raises the question about how such ensembles are affected by the choice of models and emission scenarios. Here, we aim to describe climate change in Sweden and neighbourin
A decentralized strategy for object transportation is presented, assuming that the object is grasped by a team of N cooperative manipulators. The proposed strategy consists of two steps. First, each robot estimates the wrenches applied to the object by all the others robots, even without all-to-all communication. Second, an admittance control scheme is used to limit internal wrenches, preventing e
