Robust stabilization of polytopic systems via fast and reliable neural network-based approximations

Filippo Fabiani; Paul J. Goulart

doi:10.1002/rnc.7315

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arXiv · 2204.13209

Robust stabilization of polytopic systems via fast and reliable neural network-based approximations

Filippo Fabiani·Paul J. Goulart

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Abstract

We consider the design of fast and reliable neural network (NN)-based approximations of traditional stabilizing controllers for linear systems with polytopic uncertainty, including control laws with variable structure and those based on a (minimal) selection policy. Building upon recent approaches for the design of reliable control surrogates with guaranteed structural properties, we develop a systematic procedure to certify the closed-loop stability and performance of a linear uncertain system when a trained rectified linear unit (ReLU)-based approximation replaces such traditional controllers. First, we provide a sufficient condition, which involves the worst-case approximation error between ReLU-based and traditional controller-based state-to-input mappings, ensuring that the system is ultimately bounded within a set with adjustable size and convergence rate. Then, we develop an offline, mixed-integer optimization-based method that allows us to compute that quantity exactly.

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BibTeX RIS

Filippo Fabiani, Paul J. Goulart. 2022-04-27. Robust stabilization of polytopic systems via fast and reliable neural network-based approximations. https://doi.org/10.1002/rnc.7315

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