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Stability Margins of Neural Network Controllers

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arxiv 2409.09184 v1 pith:LU5LAPTE submitted 2024-09-13 eess.SY cs.SY

Neelay Junnarkar , Murat Arcak , Peter Seiler This is my paper · ORCID
classification eess.SYcs.SY
keywords stabilitymargincontrollersmethoddiskmarginsnetworkneural
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We present a method to train neural network controllers with guaranteed stability margins. The method is applicable to linear time-invariant plants interconnected with uncertainties and nonlinearities that are described by integral quadratic constraints. The type of stability margin we consider is the disk margin. Our training method alternates between a training step to maximize reward and a stability margin-enforcing step. In the stability margin enforcing-step, we solve a semidefinite program to project the controller into the set of controllers for which we can certify the desired disk margin.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Controller Design for Bilinear Neural Feedback Loops

    eess.SY 2025-05 conditional novelty 6.0 of 10

    The paper gives LMI-based controller synthesis guaranteeing local exponential stability for bilinear systems with neural networks in the loop.

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