Aperiodic-sampled neural network controllers with closed-loop stability verifications

Renjie Ma

doi:10.1016/j.automatica.2025.112573

Aperiodic-sampled neural network controllers with closed-loop stability verifications

2025-09-08

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DOI：10.1016/j.automatica.2025.112573

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Abstract

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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 behavior 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.

Keywords:

Aperiodic-sampled control

Stability analysis

Deep neural network

Robust control

Region of attraction estimation

Aperiodic-sampled neural network controllers with closed-loop stability verifications

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