A physics-informed scenario approach selects data samples close to a physics model to reduce dataset size while constructing guaranteed barrier certificates for infinite-horizon safety of nonlinear systems.
Compositional Design of Safety Controllers for Large-Scale Stochastic Hybrid Systems
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abstract
In this work, we propose a compositional scheme based on small-gain reasoning to synthesize safety controllers for interconnected stochastic hybrid systems. In our proposed setting, we first offer an augmented scheme that characterizes each stochastic hybrid subsystem, endowed with both continuous evolution and instantaneous jumps, within a unified framework including both scenarios, implying that its state trajectories coincide with those of the original hybrid subsystem. We then introduce the concept of augmented control sub-barrier certificates (A-CSBCs) for each subsystem, thereby enabling the construction of an augmented control barrier certificate (A-CBC) for an interconnected network (from A-CSBCs of its subsystems) along with its safety controller under small-gain compositional conditions. We eventually leverage the constructed A-CBC to derive a guaranteed lower bound on the safety probability of the interconnected network. While in a monolithic scheme the computational complexity of synthesizing a control barrier certificate via sum-of-squares (SOS) optimization scales polynomially with the overall network size, the proposed compositional framework reduces this dependence to the subsystem size. We illustrate the efficacy of the proposed approach on an interconnected network comprising 1000 stochastic hybrid subsystems with nonlinear dynamics under two distinct interconnection topologies.
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2024 1verdicts
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A Physics-Informed Scenario Approach with Data Mitigation for Safety Verification of Nonlinear Systems
A physics-informed scenario approach selects data samples close to a physics model to reduce dataset size while constructing guaranteed barrier certificates for infinite-horizon safety of nonlinear systems.