Karl-Erik Årzén

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A scheduling architecture for real-time control tasks is proposed. The scheduler uses feedback from execution-time measurements and feedforward from workload changes to adjust the sampling periods of the control tasks so that the combined performance of the controllers is optimized. The performance of each controller is described by a cost function. Based(More)
The Modelica language, targeted at modeling of complex physical systems, has gained increased attention during the last decade. Modelica is about to establish itself as a de facto standard in the modeling community with strong support both within academia and industry. While there are several tools, both commercial and free, supporting simulation of(More)
Based on recent advances in control theory, we propose the notion of jitter margin for periodic control tasks. The jitter margin is defined as a function of the amount of constant delay in the control loop, and it describes how much additional time-varying delay can be tolerated before the loop goes unstable. Combined with scheduling theory, the jitter(More)
Traditional control design using MATLAB/Simulink, often disregards the temporal effects arising from the actual implementation of the controllers. Nowadays, controllers are often implemented as tasks in a real-time kernel and communicate with other nodes over a network. Consequently, the constraints of the target system, e.g., limited CPU speed and network(More)
Self-adaptation is a first class concern for cloud applications, which should be able to withstand diverse runtime changes. Variations are simultaneously happening both at the cloud infrastructure level - for example hardware failures - and at the user workload level - flash crowds. However, robustly withstanding extreme variability, requires costly(More)
The paper presents TRUETIME, a MATLAB/Simulink-based simulator for real-time control systems. TRUETIME makes it possible to simulate the temporal behavior of multi-tasking real-time kernels containing controller tasks and to study the effects of CPU and network scheduling on control performance. The simulated real-time kernel is event-driven and can handle(More)