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arXiv · 2506.18386

Aperiodic-sampled neural network controllers with closed-loop stability verifications (extended version)

Renjie Ma·Zhijian Hu·Rongni Yang·Ligang Wu
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Abstract

In this paper, we synthesize two aperiodic-sampled deep neural network (DNN) control schemes, based on the closed-loop tracking stability guarantees. By means of the integral quadratic constraint coping with the input-output behaviour of system uncertainties/nonlinearities and the convex relaxations of nonlinear DNN activations leveraging their local sector-bounded attributes, we establish conditions to design the event- and self-triggered logics and to compute the ellipsoidal inner approximations of region of attraction, respectively. Finally, we perform a numerical example of an inverted pendulum to illustrate the effectiveness of the proposed aperiodic-sampled DNN control schemes.

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Renjie Ma, Zhijian Hu, Rongni Yang, Ligang Wu. 2025-06-23. Aperiodic-sampled neural network controllers with closed-loop stability verifications (extended version). https://arxiv.org/abs/2506.18386

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