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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
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
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
The COVID-19 pandemic has impacted production and consumption patterns across the world and forced many organisations to respond. However, there is a lack of understanding as to how sharing platforms have been affected by the pandemic, how they responded to the crisis, and what kinds of long-term implications the pandemic may have on the sharing economy. This study combined systematic literature r
Empirical evidences in the library and the historian’s search tools: Union catalogues, bibliographies, databases, and the omission of Melchior Hofmann’s works from the Swedish national bibliographyThe article considers the connection between historians’ interpretations of the past and the search tools (catalogues, bibliographies, and databases) they use to access empirical source material in libra
During planar motion, contact surfaces exhibit a coupling between tangential and rotational friction forces. This article proposes planar friction models grounded in the LuGre model and limit surface theory. First, distributed planar extended state models are proposed, and the elastoplastic model is extended for multidimensional friction. Subsequently, we derive a reduced planar friction model cou
Graphical poster summary of conference paper.
Motivated by empirical research on bias and opinion formation, we formulate a multidimensional nonlinear opinion-dynamical model where agents have individual biases, which are fixed, as well as opinions, which evolve. The dimensions represent competing options, of which each agent has a relative opinion, and are coupled through normalization of the opinion vector. This can capture, for example, an
This work demonstrates the utility of dual-arm robots with dual-wrist force-torque sensors in manipulating a Deformable Linear Object (DLO) within an unknown environment that imposes constraints on the DLO’s movement through contacts and fixtures. We propose a strategy to estimate the pose of unknown environmental contacts encountered during the manipulation of a DLO, classifying the induced const
This article addresses the obstacle avoidance problem for setpoint stabilization tasks in complex dynamic 2-D environments that go beyond conventional scenes with isolated convex obstacles. A combined motion planner and controller is proposed that integrates the favorable convergence characteristics of closed-form motion planning techniques with the intuitive representation of system constraints t
The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of electrodes that balances performance and usability constraints. The search for the optimal subset of electrodes from a larger set is a problem wit
The AI Act’s (AIA) requirements for high-risk AI systems affect many aspects of modern software systems. Knowing which AIA-related technical challenges are relevant to different companies is essential to focus compliance-oriented research on the aspects that matter. We therefore conducted an interview study in collaboration with a case company that specializes in network video solutions within the
Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give near-optimal performance. More specifically, we consider networked linear-quadratic controllers with decoupled costs and spatially exponentially decaying dynamics
Frequent updates in IoT software are crucial for fixing security vulnerabilities, correcting bugs, and adding new features. However, for systems comprising geographically distributed devices, implementing updates is challenging. Such updates must be coordinated across multiple devices, automated without end-user involvement, adaptable to weak connectivity, and minimally disruptive to end users. In
In this work, we consider the problem of coordinating a collection of nth-order integrator systems. The coordination is achieved through the novel serial consensus design; this control design achieves a stable closed-loop system while adhering to the constraint of only using local and relative measurements. Earlier work has shown that second-order serial consensus can stabilize a collection of dou
The optimization of expensive-to-evaluate black-box functions is prevalent in various scientific disciplines. Bayesian optimization is an automatic, general and sample-efficient method to solve these problems with minimal knowledge of the underlying function dynamics. However, the ability of Bayesian optimization to incorporate prior knowledge or beliefs about the function at hand in order to acce
System logs are crucial for understanding the state and health of systems, yet manual inspection becomes impractical due to the high volume of messages. Consequently, machine learning-based log anomaly detection has emerged to automatically identify irregularities. This study investigates the effectiveness of log message embeddings, a novel parsing method, for anomaly detection in complex systems.
