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arXiv · 2603.17134

Neural-NPV Control: Learning Parameter-Dependent Controllers and Lyapunov Functions

MD Abul Kashem Niloy·Adam Hallmark·Yikun Cheng·Pan Zhao
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Abstract

This paper presents Neural-NPV Control, a learning-based framework for joint synthesis of a parameter-dependent (PD) controller and a PD Lyapunov function using neural networks for an NPV system under input constraints. At the first stage, the proposed framework utilizes a gradient-based counterexample-guided procedure to synthesize a PD controller and a PD Lyapunov function candidate. The second stage relies on a level-set guided procedure to refine the controller and Lyapunov function candidate while maximizing the robust region of attraction (R-ROA). The learned controller, Lyapunov function, and R-ROA are empirically evaluated. We demonstrate the advantages of Neural-NPV over SOS-based methods in terms of applicability, performance, and scalability through numerical experiments involving a simple inverted pendulum with one scheduling parameter and a quadrotor system with three scheduling parameters.

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MD Abul Kashem Niloy, Adam Hallmark, Yikun Cheng, Pan Zhao. 2026-03-17. Neural-NPV Control: Learning Parameter-Dependent Controllers and Lyapunov Functions. https://doi.org/10.1109/lcsys.2026.3713238

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