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New method uses invertible neural networks for certified feedforward control

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method for certifying data-driven feedforward control for unknown nonlinear systems, particularly for periodic tracking with partial state measurements. The approach utilizes an invertible neural network (INN) as a surrogate model, which simplifies the process by avoiding complex nonconvex inversion problems. By applying conformal prediction to estimate surrogate modeling errors, the method provides finite-sample probabilistic guarantees and marginal certificates on feedforward tracking error. This technique was demonstrated on a DC motor with nonlinear friction. AI

IMPACT This research could lead to more reliable and certifiable AI-driven control systems in robotics and automation.

RANK_REASON The cluster contains an academic paper detailing a new control method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method uses invertible neural networks for certified feedforward control

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The cluster contains an academic paper detailing a new control method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Berk Altiner, Rajasree Sarkar, Arunava Banerjee, Zongxuan Sun, Kenneth Kim ·

    Certified Feedforward Tracking for Unknown Nonlinear Systems via Invertible Neural Networks

    arXiv:2608.06419v1 Announce Type: cross Abstract: In this paper, we address the certification of datadriven feedforward control for periodic tracking of unknown nonlinear systems under partial state measurements. To this end, we adopt an invertible neural network (INN) as a surro…

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