arXiv · 2409.09184
Stability Margins of Neural Network Controllers
Abstract
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.
Explore related subjects
Keep this discovery
Neelay Junnarkar, Murat Arcak, Peter Seiler. 2024-09-13. Stability Margins of Neural Network Controllers. https://doi.org/10.23919/acc63710.2025.11107746
Cite the original work for its findings. Save a collection to share your selection of sources.