A Sampling-Based Approach to Scalable Constraint Satisfaction in Linear Sampled-Data Systems - Part I: Computation

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Provided by: University of Calgary
Topic: Big Data
Format: PDF
Sampled-Data (SD) systems, which are composed of both discrete- and continuous-time components, are arguably one of the most common classes of cyber-physical systems in practice; most modern controllers are implemented on digital platforms while the plant dynamics that are being controlled evolve continuously in time. As with all cyber-physical systems, ensuring hard constraint satisfaction is key in the safe operation of SD systems. A powerful analytical tool for guaranteeing such constraint satisfaction is the viability kernel: the set of all initial conditions for which a safety-preserving control law (that is, a control law that satisfies all input and state constraints) exists.
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