Scalable Incremental Robustness Analysis of Neural Network Feedback Systems

Abstract:Semidefinite programming (SDP) certificates for feedback systems containing deep neural networks (NNs) typically scale with the total number of neurons, whereas small-gain tests are scalable but can be highly conservative. This paper develops a unified and scalable framework for incremental robust stability and performance analysis of feedback interconnections involving high-dimensional NNs and unmodeled dynamics. By combining a structured decomposition of the full-order SDP condition with scalable Lipschitz constant estimation algorithms, we derive reduced verification conditions that certify incremental convergence and incremental $\ell_2$-gain bounds. The dimensions of the resulting control-analysis linear matrix inequalities (LMIs) depend only on the widths of the last two network layers and are \textit{independent of network depth}. The framework preserves the coupling between the plant and the NN, with the incremental small-gain condition recovered as a special case. To further reduce conservatism, we develop a multi-round alternating update scheme that iteratively refines the coupling variables while preserving scalability. Numerical experiments show that the proposed framework achieves state-of-the-art incremental $\ell_2$-gain bounds for large-scale NN feedback systems.

Optimization and Control (math.OC); Machine Learning (cs.LG); Systems and Control (eess.SY)

arXiv:2609.22576 [math.OC]

(or arXiv:2609.22576v1 [math.OC] for this version)

https://doi.org/10.48550/arXiv.2609.22576 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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