Stability Verification of Neural Network Controllers Using Mixed-Integer Programming

We propose a framework for the stability verification of Mixed-Integer Linear Programming (MILP) representable control policies. This framework compares a fixed candidate policy, which admits an efficient parameterization and can be evaluated at a low computational cost, against a fixed baseline pol...

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Vydané v:	IEEE transactions on automatic control Ročník 68; číslo 12; s. 1 - 16
Hlavní autori:	Schwan, Roland, Jones, Colin N., Kuhn, Daniel
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Approximation Approximation error Artificial neural networks Closed loops Computing costs Integer programming Linear programming Mathematical analysis Mixed integer Neural networks Numerical stability Parameterization Policies Power converters Power electronics Predictive control Programming Quadratic programming Stability analysis Symmetric matrices Verification
ISSN:	0018-9286, 1558-2523
On-line prístup:	Získať plný text
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