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Embedded systems often operate in so variable con- ditions that design can only be carried out for some worst-case scenario. This leads to over-provisioned resources, and undue power consumption. Feedback control is an effective (and not yet fully explored) way to tailor resource usage online, thereby making the system behave and consume as if it was optimized for each specific utilization case. A
In this paper, we explore positivity preserving model reduction. The reduction is performed by truncating the states of the original system without balancing in the classical sense. This may result in conservatism, however, this way the physical meaning of the individual states is preserved. The reduced order models can be obtained using simple matrix operations or using distributed optimization m
The Covid-19 pandemic has spawned numerous dynamic modeling attempts aimed at estimation, prediction, and ultimately control. The predictive power of these attempts has varied, and there remains a lack of consensus regarding the mechanisms of virus spread and the effectiveness of various non-pharmaceutical interventions that have been enforced regionally as well as nationally. Setting out in data
We show that algebraic proofs are hard to find: Given an unsatisfiable CNF formula F, it is NP-hard to find a refutation of F in the Nullstellensatz, Polynomial Calculus, or Sherali-Adams proof systems in time polynomial in the size of the shortest such refutation. Our work extends, and gives a simplified proof of, the recent breakthrough of Atserias and Müller (JACM 2020) that established an anal
Automatic tuning of PID controllers using relay feedback experiments has received attention on and off since it was first proposed and industrially implemented in a control system in the 1980s. While optimal experiment design and modern system identification easily outperform the original automatic tuner, they rely on computational resources that are not always available in industrial control syst
The increased use of cloud and other large scale datacenter IT services and the associated power usage has put the spotlight on more energy-efficient datacenter management. In this paper, a simple model was developed to represent the heat rejection system and energy usage in a small DC setup. The model was then controlled by a reinforcement learning agent that handles both the load balancing of th
In order to produce models for automatic controller tuning, this paper proposes a method that combines a short experiment with a novel scheme for approximating processes using low-order time-delayed models. The method produces models aimed to tune PI and PID controllers, but they could also be used for other model-dependent controllers like MPC. The proposed method has been evaluated in simulation
We study an optimal control problem for a simple transportation model on a path graph. We give a closed form solution for the optimal controller, which can also account for planned disturbances using feed-forward. The optimal controller is highly structured, which allows the controller to be implemented using only local communication, conducted through two sweeps through the graph.
When deploying networked computing-based applications, proper resource management of the server-side resources is essential for maintaining quality of service and cost efficiency. The work presented in this thesis is based on six papers, all investigating problems that relate to resource management of server-based systems. Using a queueing system approach we model the performance of a database sys
This thesis addresses challenges in increasing the robustness of cloud-deployed applications and services to unexpected events and dynamic workloads. Without precautions, hardware failures and unpredictable large traffic variations can quickly degrade the performance of an application due to mismatch between provisioned resources and capacity needs. Similarly, disasters, such as power outages and
Human motion analysis in images and video, with its deeply inter-related 2D and 3D inference components, is a central computer vision problem. Yet, there are no studies that reveal how humans perceive other people in images and how accurate they are. In this paper we aim to unveil some of the processing—as well as the levels of accuracy—involved in the 3D perception of people from images by assess
Cloud computing has dramatically simplified the deployment of new software and, indeed, the number of applications that are hosted by cloud providers every day is increasing. The data center owner should provide computing capacity to a set of customers, each of them powering up and down virtual machines dynamically, to handle variations in the incoming requests. Cloud providers, however, should al
We consider an iterative learning control (ILC) approach to machining with industrial robots. The robot and the milling process are modeled using system identification methods with a data-driven approach. Two different model-based ILC algorithms are proposed and subsequently experimentally verified in a milling scenario. The difference between the two approaches is the required sensors for acquiri
The emerging new idea of lane-keeping electronic stability control is investigated. In a critical situation, such as entering a road curve at excessive speed, the optimal behavior may differ from the behavior of traditional ESC, for example, by prioritizing braking over steering response. The important question that naturally arises is if this has a significant effect on safety. The main contribut
Many of the classical approaches to controller synthesis do not scale well for large and complex systems. This is mainly due to computational complexity and the lack of distributed structure in the resulting controllers. It is important that limitations on the information given and processed by sensors and actuators can be incorporated into the design procedure. However, such constraints may great
We consider a real-time system of multiple tasks, each task having a plant to control. The overall quadratic control cost is to be optimized. We exploit the periodicity of the task response time, which corresponds to a periodic delay pattern in the feedback control loop. Perturbed periods are used as a tool to find a finite hyperperiod. We present an analytical procedure to design a periodic linea
This paper presents a straightforward method to convert non-uniformly sampled data to uniform samples in order to be processed by some off-line techniques such as system identification. In such scenarios, we deal with a finite-length measurement sequence. We assume periodic extension of the signals, which results in a simple system of linear equations. Furthermore, we take into account a number of
