Data-Driven Discrete-Time Control Barrier Functions
Kaviani, Farzan
Kaviani, Farzan
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Abstract
This paper presents a data-driven framework for enforcing constraints on discrete-time linear systems with unknown dynamics, operating under real-time (potentially unsafe) controllers. Using measured input-output data from the closed-loop system, we first compute a safe control-invariant set for the underlying unknown plant. This set is then used to construct a discrete-time data-driven control barrier function (D3CBF) directly from data, without an explicit model. The resulting D3CBF is embedded within a real-time optimization-based filter that minimally modifies the output of the nominal controller to enforce safety constraints. Numerical simulations demonstrate the effectiveness of the approach.
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Date
1/1/2026
Student Status
Graduate Student
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Poster
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Electrical Engineering
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College of Engineering and Mathematical Sciences
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Engineering