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A new approach to feedback control design based on optimal control is proposed. Instead of expensive computations of the value function for different penalties on the states and inputs, we use a control Lyapunov function that amounts to be a value function of the optimal control problem with suitable cost design and then study combinations of input and state penalty that are compatible with this v

Autonomous vehicles hold promise for increased vehicle and traffic safety, and there are several developments in the field where one example is an avoidance maneuver. There it is dangerous for the vehicle to be in the opposing lane, but it is safe to drive in the original lane again after the obstacle. To capture this basic observation, a lane-deviation penalty (LDP) objective function is devised.

A controller for critical vehicle maneuvering is proposed that avoids obstacles and keeps the vehicle on the road while achieving heavy braking. It operates at the limit of friction and is structured in two main steps: a motion-planning step based on receding-horizon planning to obtain acceleration-vector references, and a low-level controller for following these acceleration references and transf

Distributed surveillance systems typically consist of multiple cameras that need to store some fraction of their video streams at a central storage node. The disk space of this node constitutes a shared resource. In the paper the disk space allocation is formulated as a PI control problem and a new method for enforcing global resource constraints inspired by anti-windup tracking is proposed. The a

One of the most significant obstacles for the every­day use of systems based on Brain­Computer Inter­faces (BCIs) is the tediousness of calibration. Successful improvements on calibration, particularly the time needed and the user­experience, have been made with, e.g., transfer learning, gamification, and task estimation [1, 2, 3]. Inthis work, we present an adaptive approach to BCI systems’ calib

This article addresses the problem of basis correction in the context of linear switched-system (LSS) identification from input–output data. It is often the case that identification algorithms for the LSSs from input–output data operate locally. The local submodel estimates, identified individually by subspace algorithms from the input-output data, reside in different-state bases, which mandates p

This special section of the IEEE OPEN JOURNAL OF CIRCUITS AND SYSTEMS (OJCAS) is dedicated to highlight the state-of-the-art research progress on circuits, systems, and algorithms associated with the design of beyond 5G (B5G) and 6G wireless systems.

The adaptive resource management framework implemented in the European ACTORS project is presented. A resource manager has been developed that collaborates with a new Linux scheduler providing support for hard constant bandwidth server reservations, in order to adapt applications to changes in resource availability and to adapt the resource allocation to changes in application requirements. The fo

Self-adaptation enables software to execute successfully in dynamic, unpredictable, and uncertain environments. Control theory provides a broad set of mathematically grounded techniques for adapting the behavior of dynamic systems. While it has been applied to specific software control problems, it has proved difficult to define methodologies allowing non-experts to systematically apply control te

Shift-map image processing is a new framework based on energy minimization over a large space of labels. The optimization utilizes -expansion moves and iterative refinement over a Gaussian pyramid. In this paper we extend the range of applications to image registration. To do this, new data and smoothness terms have to be constructed. We note a great improvement when we measure pixel similarities

In the study of distributed systems, it is of fundamental interest to understand how specifications on local components and interconnections influence global properties of the system. In this paper, we consider linear continuous time systems described by sparse matrices. Properties of interest are stability and quadratic performance specifications such as passivity and input-output gain. In partic

It has been shown that optimal controller synthesis for positive systems can be formulated as a linear program. Leveraging these results, we propose a scalable iterative algorithm for the systematic design of sparse, small gain feedback strategies that stabilize the evolutionary dynamics of a generic disease model. We achieve the desired feedback structure by augmenting the optimization problems w