AI & ML
Input-to-State Safety and Small-Gain Theorems for Large-Scale Nonlinear Systems
arXiv cs.SY0 views
arXiv:2611.13718v1 Announce Type: new Abstract: This paper studies the safety verification problem for a general class of large-scale systems characterized by multiple safety indicator functions. Each safety indicator function satisfies a trajectory-based input-to-state safety (ISSf) estimate that provides a lower bound on its value along system trajectories in terms of the initial state, external inputs, and the other safety indicator functions, whose influence is quantified by continuous and nondecreasing gain functions. Within this framework, we establish a safety-oriented small-gain theorem and its corresponding corollaries that enable compositional and scalable safety verification of the interconnected system, provided that the interconnection gain functions satisfy a small-gain condition on the negative real axis. This condition concerns only the neighborhood of the safety boundary, thereby facilitating the application of our results to safety verification of multi-agent systems under distributed control. Our proof combines small-gain techniques with the lower-bound structure of ISSf estimates and develops an inductive comparison argument that establishes the ISSf property for the overall system throughout the maximal interval of existence. As an application, we establish robust safety for the natural distributed "half-spring" controller, theoretically supporting the computationally efficient and inspiring approach to safety-critical control of multi-agent systems in the presence of obstacles and external disturbances.
Source:https://arxiv.org/abs/2611.13718PreviousDecision-Focused Learning via Neural Optimization Layers for Battery-Assisted Grid-Import Smoothing in AI Data CentersNextContinuous-Time Modeling of Destination-Constrained Dynamic Systems via Doob's $h$-Transform
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