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In this paper we provide an analytical solution to an H2 optimal control problem, that applies whenever the process corresponds to a uniformly damped network of masses and springs. The solution covers both stable and unstable systems, and illustrates analytically how damping affects the levels of achievable performance. Furthermore, the resulting optimal controllers can be synthesised using passiv
When deploying deep neural networks on robots or other physical systems, the learned model should reliably quantify predictive uncertainty. A reliable uncertainty allows downstream modules to reason about the safety of its actions. In this work, we address metrics for uncertainty quantification. Specifically, we focus on regression tasks, and investigate Area Under Sparsification Error (AUSE), Cal
Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiologic
This paper concerns control of a class of systems where multiple dynamically stable agents share a nonlinear and bounded control-interconnection. The agents are subject to a disturbance which is too large to reject with the available control action, making it impossible to stabilize all agents in their desired states. In this nonlinear setting, we consider two different anti-windup equipped propor
This paper investigates the use of decentralised control architectures with heterogeneous dynamics for improving performance in large-scale systems. Our focus is on two well-known decentralised approaches; the 'predecessor following' and 'bidirectional' architectures for vehicle platooning. The former, utilising homogeneous control dynamics, is known to face exponential growth in disturbance ampli
Certainty equivalence adaptive controllers are analysed using a “data-driven Riccati equation”, corresponding to the model-free Bellman equation used in Q-learning. The equation depends quadratically on data correlation matrices. This makes it possible to derive simple sufficient conditions for stability and robustness to unmodeled dynamics in adaptive systems. The paper is concluded by short rema
The strategy of pre-training a large model on a diverse dataset, then fine-tuning for a particular application has yielded impressive results in computer vision, natural language processing, and robotic control. This strategy has vast potential in adaptive control, where it is necessary to rapidly adapt to changing conditions with limited data. Toward concretely understanding the benefit of pre-tr
The advances in highly automated and autonomous transportation systems over the last decade have generated great interest in topics in the safe navigation of land vehicles. With distributed control strategies employed in the majority of applications of autonomous vehicles, such as traffic and formation control, the much-required resilience takes the form of fault-tolerance with respect to informat
This letter presents and analyzes an adaptive data-driven controller that learns the optimal processing rate in a multi-unit processing network in the presence of disturbances. We formulate an optimization problem of linear cost, linear dynamics for the processing network model and an affine constraint on the dispatcher policy. A data-driven linear equation is constructed, based on which the onlin
We present a method for optimal control with respect to a linear cost function for positive linear systems with coupled input constraints. We show that the Bellman equation giving the optimal cost function and resulting sparse state feedback for these systems can be stated explicitly, with the solution given by a linear program. Our framework admits a range of network routing problems with underly
We study dynamical optimal transport of discrete time systems (dDOT) with Lagrangian cost. The problem is approached by combining optimal control and Kantorovich duality theory. Based on the derived solution, a first order splitting algorithm is proposed for numerical implementation. While solving partial differential equations is often required in the continuous time case, a salient feature of ou
We investigate optimal mass transport problem of affine-nonlinear dynamical systems with input and density constraints. Three algorithms are proposed to tackle this problem, including two Uzawa-type methods and a splitting algorithm based on the Douglas-Rachford algorithm. Some preliminary simulation results are presented to demonstrate the effectiveness of our approaches.
Language exposure and use (LEU) are widely viewed as key factors in multilingual development, and research highlights the importance of considering not just the frequency and quantity of LEU, but also contextual factors such as when and where a language is used, with whom and why. In this study, we illustrate the complexity of LEU in two contexts (study abroad and migration) by applying sequential
A wideband balun-LNA featuring common gate (CG) – common source (CS) noise cancelling, current reuse, and g0.8 GHz to 7.3 GHz that fulfills the requirements of sub-6 GHzm -boosting is presented. It covers frequency range from 5G new radio (NR) technology. The LNA provides balanced output for identical differential loads and employs forward body biasing for linearity improvement. The LNA has low se
This letter gives a closed-form expression for an H-infinity optimal controller with diagonal gain matrix. This phenomenon occurs for certain network systems with acyclic graphs, and potential applications include irrigation networks. Moreover, the above is identified as a special case of a particular controller structure which is shown to be H-infinity optimal if the controller and the resulting
In this letter we derive fundamental limitations on the levels of H {2} and H {\infty {}} performance that can be achieved when controlling lossless systems. The results are applied to the swing equation power system model, where it is shown that the fundamental limit on the H {2} norm scales with the inverse of the harmonic mean of the inertias in the system. This indicates that power systems may
Coastal protection has evolved from focusing on hard solutions such as breakwaters and groynes to include soft or nature-based solutions (NbS). NbS have been pro- posed as cost-effective means to offer long-term coastal protection and at the same time strengthen coastal resilience and biodiversity. However, projects utilizing NbS for coastal protection have often focused on a single solution and t
This paper presents a reconfigurable radio frequency front-end (RFFE) tailored for direct RF sampling receivers operating within Frequency Range 1 (FR-1) of the 5G spectrum. It consists of a balun-LNA, a noise-cancelling, current-reuse, Q-enhanced filter, and a programmable gain amplifier (PGA). Fabricated in 22-nm FD-SOI technology, the RFFE covers the entire frequency range from 1.7 to 6.4 GHz w
The need for autonomous robot systems in both the service and the industrial domain is larger than ever. In the latter, the transition to small batches or even “batch size 1” in production created a need for robot control system architectures that can provide the required flexibility. Such architectures must not only have a sufficient knowledge integration framework. It must also support autonomou
With the growing demand for efficient, safe and sustainable transportation systems, the imperative to design intelligent routing and traffic management solutions within urban settings, requiring minimal data exchange and ensuring scalability, becomes evident. This paper introduces an innovative paradigm for traffic management. By seamlessly integrating machine learning and Autonomous Intersection
