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In digital control systems, the state is sampled at given sampling instants and the input is kept constant between two consecutive instants. With the optimal sampling problem, we mean the selection of sampling instants and control inputs, such that a given function of the state and input is minimized. In this paper, we formulate the optimal sampling problem and we derive a necessary condition of t
Stochastic optimal power flow can provide the system operator with adequate strategies for controlling the power flow to maintain secure operation under stochastic parameter variations. One limitation of stochastic optimal power flow has been that only limits on lineflows have been used as stability constraints. In many systems voltage stability and small-signal stability also play an important role i
This paper proposes a transfer function parameter identification method, applicable to SISO systems of any order. Parameter identification is posed as a (non-convex) squared output error minimization problem, numerically solved utilizing Newton-Raphson iteration with back tracking line search. Focus lies on computing the cost function gradient and Hessian with respect to the parameter vector and o
Innovative engineering methods are needed to enhance the adaptability and agility of industrial control procedures and concurrently manage their rising complexity. Service-oriented Architecture (SOA) constitutes a promising paradigm to meet these challenges. To apply the rather abstract SOA principles to industrial automation, a model-driven engineering method is presented in this paper. Therefore
Larger amounts of variable renewable energy sources bring about larger amounts of uncertainty in the form of forecast errors. When taking operational and planning decisions under uncertainty, a tradeoff between risk and costs must be made. Today's deterministic operational tools, such as N-1-based methods, cannot directly account for the underlying risk due to uncertainties. Instead, several defin
For monotone systems evolving on the positive orthant, two types of Lyapunov functions are considered: Sum- and max-separable Lyapunov functions. One can be written as a sum, the other as a maximum of functions of scalar arguments. Several constructive existence results for both types are given. Notably, one construction provides a max-separable Lyapunov function that is defined at least on an arb
Abstract in UndeterminedThis manuscript presents a new approach to the design of task scheduling algorithms, where system-theoretical methodologies are used throughout. The proposal implies a significant perspective shift with respect to mainstream design practices, but yields large payoffs in terms of simplicity, flexibility, solution uniformity for different problems, and possibility to formally
Resource reservation is a technique that allows isolating applications from interfering among each other. In the most classic setting, this method requires the periodic allocation of a given budget of resource over time. However, in reality, the actual budget allocation may deviate from its ideal value. Examples of causes of this deviation are: the presence of a system tick, the usage of shared re
In this theoretical paper we consider the problem of accurately triangulating a scene plane. Rather than first triangulating a set of points and then fitting a plane to these points, we try to minimize the back-projection errors as functions of the plane parameters directly. As this is both geometrically and statistically meaningful our method performs better than the standard two step procedure.
This work presents an event-based control structure using the generalized predictive control (GPC) algorithm with actuator deadband. The main objective of this work is to limit the number of controlled system updates. In this approach, the controlled process is sampled with a constant sampling time and is updated in an asynchronous way that depends on the obtained control signal value. To achieve
Gradient orientations are a common feature used in many computer vision algorithms. It is a good feature when the gradient magnitudes are high, but can be very noisy when the magnitudes are low. This means that some gradient orientations are matched with more confidence than others. By estimating this uncertainty, more weight can be put on the confident matches than those with higher uncertainty.
We analyze reinforcement learning under so-called "dynamic reinforcement." In reinforcement learning, each agent repeatedly interacts with an unknown environment (i.e., other agents), receives a reward, and updates the probabilities of its next action based on its own previous actions and received rewards. Unlike standard reinforcement learning, dynamic reinforcement uses a combination of long-ter
The phenomenon of adopting open source software development practices in a corporate environment is known by many names, one being inner source. The objective of this study is to investigate how an organization consisting of small development teams can benet from adopting inner source and assess the level of applicability. The research has been conducted as a case study at a software development c
Nowadays, the same piece of code should run on different architectures, providing performance guarantees in a variety of environments and situations. To this end, designers often integrate existing systems with ad-hoc adaptive strategies able to tune specific parameters that impact performance or energy—for example, frequency scaling. However, these strategies interfere with one another and unpred
Stochastic optimal power flow can provide the system operator with adequate strategies for controlling the power flow to maintain secure operation under stochastic parameter variations. One limitation of stochastic optimal power flow has been that only steady-state variable limits have been used as security constraints. In many systems voltage stability and small-signal stability also play an impo
Decentralised and scalable conditions for robust stability of networks of heterogenous linear time-invariant (LTI) systems are derived based on integral quadratic constraints. These generalise previous works in the literature with an increased flexibility in the choice of multipliers employed. The results allow for arbitrary interconnection matrices and accommodate multi-input-multi-output systems
When a feedback controller is implemented in a networked embedded system, the computations and communications induce delays and jitter, which may destabilize the control loop. The majority of previous work on analysis of control loops with time-varying delays has focused on output (actuation) jitter. In many embedded systems, input (sampling) jitter is also an issue. In this paper, we analyze the
This paper presents a novel on-line approach of merging multiple different predictors of plasma glucose into a single optimized prediction. Various different predictors are merged by recursive weighting into a single prediction using regularized optimization. The approach is evaluated on 12 data sets of type I diabetes data, using three parallel predictors. The performance of the combined predicti
