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

• URL: http://arxiv.org/abs/2204.13209v2
• Date: Tue, 23 Jan 2024 17:31:48 GMT
• Title: Robust stabilization of polytopic systems via fast and reliable neural network-based approximations
• Authors: Filippo Fabiani, Paul J. Goulart
• Abstract summary: We consider the design of fast and reliable neural network (NN)-based approximations of traditional stabilizing controllers for linear systems with polytopic uncertainty. We 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.
• Score: 2.2299983745857896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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