Dan Henriksson

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Control systems are becoming increasingly complex from both the control and computer science perspectives. Today, even seemingly simple embedded control systems often contain a multitasking real-time kernel and support networking. At the same time, the market demands that the cost of the system be kept at a minimum. For optimal use of computing resources,(More)
Despite availability of multiple orthogonal communication channels on common sensor network platforms, such as MicaZ motes, and despite multiple simulation-supported designs of multi-channel MAC protocols, most existing sensor networks use only one channel for communication, which is a source of bandwidth inefficiency. In this work, we design, implement,(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)
The paper presents a feedback scheduling strategy for multiple control tasks that uses feedback from the plant states to distribute the computing resources optimally among the tasks. Linear-quadratic controllers are analyzed, and expressions relating the expected cost to the sampling period and the plant state are derived and used for on-line samplerate(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)
The paper presents two recently developed, MATLAB-based analysis tools for real-time control systems. The first tool, called JITTERBUG, is used to compute a performance criterion for a control loop under various timing conditions. The tool makes it easy to quickly judge how sensitive a controller is to implementation effects such as slow sampling, delays,(More)
The paper presents some preliminary results on dynamic scheduling of model predictive controllers (MPC’s). In model predictive control, the control signal is obtained by optimization of a cost function in each sample, and the MPC task may experience very large variations in execution time. Unique to this application, the cost function also offers an(More)
The increased complexity of performance-sensitive software systems leads to increased use of automated adaptation policies in lieu of manual performance tuning. Composition of adaptive components into larger adaptive systems, however, presents challenges that arise from potential incompatibilities among the respective adaptation policies. Consequently,(More)