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This paper considers robust stability analysis of a large network of interconnected uncertain systems. To avoid analyzing the entire network as a single large, lumped system, we model the network interconnections with integral quadratic constraints. This approach yields a sparse linear matrix inequal- ity which can be decomposed into a set of smaller, coupled linear matrix inequalities. This allow
This paper reviews the 25-plus year history of significant developments of the GENESIS shoreline response model. Topics discussed are line sources and sinks of sand, representation of shore-normal structures including natural sand bypassing, wave transmission by and shoreline response to shore-parallel structures, seawalls, migrating longshore sand waves, seasonal variation by cross-shore sand tra
This thesis presents an improved version of the classic relay autotuner. The proposed autotuner uses an asymmetric relay function to better excite the process in the experiment phase. The improved excitation provides the possibility to obtain better models and hence better tuning, without making the autotuner more complicated or time consuming.Some processes demand more accurate modeling and tunin
The number of applications sharing the same embedded device is increasing dramatically. Very efficient mechanisms (resource managers) for assigning the CPU time to all demanding applications are needed. Unfortunately, existing optimization-based resource managers consume too much resource themselves. In this paper, we address the problem of distributed convergence to fair allocation of CPU resourc
Model predictive control (MPC) is applied to a physical pendulum system consisting of a pendulum and a cart. The objective of the MPC controller is to steer the system towards precomputed, time-optimal feedforward trajectories that move the system from one stationary point to another. The sample time of the controller sets hard limitations on the execution time of the optimization algorithm in the
In automotive systems, some of the engine control tasks are triggered by specific crankshaft rotation angles and are designed to adapt their functionality based on the angular velocity of the engine. This paper proposes a new task model for specifying such a type of real-time activities and presents an approach for analyzing the system feasibility under dynamic scheduling for different scenarios.
This paper presents a novel online approach of merging multiple different predictors of time-varying dynamics into a single optimized prediction. Different predictors are merged by recursive weighting into a single prediction using regularized optimization. The approach is evaluated on two different cases of data with shifting dynamics; one example of prediction using several approximate models of
In this article we consider how the operator of an electric power system should activate bids on the regulating power market in order to minimize the expected operation cost. Important characteristics of the problem are reaction times of actors on the regulating market and ramp-rates for production changes in power plants. Neglecting these will in general lead to major underestimation of the opera
High-performance embedded systems require the execution of many applications on multicore platforms and are subject to stringent restrictions and constraints. The actors project approach provides temporal isolation through resource reservation over a multicore platform, adapting the available resources on the basis of the overall quality requirements. The architecture is fully operational on both
Urban trees are uniquely valuable in towns and cities for the ecosystem services they provide their citizens and can as such be considered a potent source of public goods and positive externalities - even when these trees are found on private properties. The importance of urban trees will only grow as events of severe heat and extreme rainfall become more and more frequent and the urgency to reduc
Sudden cardiac arrest is the second most common cause of death in Sweden, following tumors. Annually, 10 000 people are subject to sudden cardiac arrest outside of hospital in the country.Following sudden cardiac arrest, blood circulation in the body ceases, and the brain is subject to irreversible damage within minutes. The treatment consists mainly of mechanical chest compressions to circulate b
We reconsider the variational problem of finding the shape of a vertically hanging rope such that its elongation, due to the rope’s own weight and that of a load attached at its lower end, is minimum. The known solution is recalled and the missing proof of optimality is supplied.
We analyze the local convergence of proximal splitting algorithms to solve optimization problems that are convex besides a rank constraint. For this, we show conditions under which the proximal operator of a function involving the rank constraint is locally identical to the proximal operator of its convex envelope, hence implying local convergence. The conditions imply that the non-convex algorith
Cloud computing infrastructures are powering most of the web hosting services that we use at all times. A recent failure in the Amazon cloud infrastructure made many of the website that we use on a hourly basis unavailable1. This illustrates the importance of cloud applications being able to absorb peaks in workload, and at the same time to tune their power requirements to the power and energy cap
In this paper we consider L1 optimal and H-infinity optimal control problems for a particular class of Positive Bilinear Systems that arise in drug dosage design for HIV treatment. Starting from existent characterizations of the L1-norm for positive systems, a convex formulation for the first problem is provided. As for the H-infinity case, we propose an algorithm based on the iterative solution o
In the framework of robotics, Reinforcement Learning (RL) deals with the learning of a task by the robot itself. This paper presents a hierarchical planning approach in which the robot learns the optimal behavior for different levels. For high-level discrete actions, Q-learning was chosen, whereas for the low level we utilize Policy Improvement with Path Integrals (PI^2) algorithm to learn the par
