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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Veröffentlicht in:	IEEE transactions on automatic control Jg. 68; H. 12; S. 1 - 16
Hauptverfasser:	Schwan, Roland, Jones, Colin N., Kuhn, Daniel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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