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Data-Driven Distributed Safe Control Design for Multi-Agent Systems
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DOI：10.1002/acs.4088.png)
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Abstract
En 中文
This paper presents a data-driven control barrier function (CBF) technique for ensuring safe control of multi-agent systems (MASs) with uncertain linear dynamics. A data-driven quadratic programming (QP) optimization is first developed for CBF-based safe control of single-agent systems using a nonlinear controller. This approach is then extended to the distributed safe control of MASs. To bypass system identification, the closed-loop dynamics are represented using collected data, and the safety constraints are imposed on this closed-loop representation. This data-efficient representation is subsequently integrated into QP optimizations, resulting in data-driven QP formulations that learn the closed-loop systems and their corresponding controllers, ensuring the safety of the MASs. As a result, the presented CBF-based approach designs a safe controller based solely on input, state, and state-derivative measurements without requiring knowledge of the underlying dynamics of the agents. Furthermore, for the special case of linear controllers, we show that the need for state-derivative measurements can be eliminated. We also show that the sample complexity of learning closed-loop dynamics is less than that of its model-based counterpart, which relies on open-loop system identification. The simulation results for a safe formation control problem demonstrate the efficacy of the proposed approach for the MASs.
Keywords:
control barrier functions (CBFs)
data-driven formulation
multi-agent systems (MASs)
safety
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