{"ID":2876963,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.20367","arxiv_id":"2508.20367","title":"Delay-adaptive Control of Nonlinear Systems with Approximate Neural Operator Predictors","abstract":"In this work, we propose a rigorous method for implementing predictor feedback controllers in nonlinear systems with unknown and arbitrarily long actuator delays. To address the analytically intractable nature of the predictor, we approximate it using a learned neural operator mapping. This mapping is trained once, offline, and then deployed online, leveraging the fast inference capabilities of neural networks. We provide a theoretical stability analysis based on the universal approximation theorem of neural operators and the transport partial differential equation (PDE) representation of the delay. We then prove, via a Lyapunov-Krasovskii functional, semi-global practical convergence of the dynamical system dependent on the approximation error of the predictor and delay bounds. Finally, we validate our theoretical results using a biological activator/repressor system, demonstrating speedups of 15 times compared to traditional numerical methods.","short_abstract":"In this work, we propose a rigorous method for implementing predictor feedback controllers in nonlinear systems with unknown and arbitrarily long actuator delays. To address the analytically intractable nature of the predictor, we approximate it using a learned neural operator mapping. This mapping is trained once, off...","url_abs":"https://arxiv.org/abs/2508.20367","url_pdf":"https://arxiv.org/pdf/2508.20367v1","authors":"[\"Luke Bhan\",\"Miroslav Krstic\",\"Yuanyuan Shi\"]","published":"2025-08-28T02:30:53Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.LG\",\"math.DS\"]","methods":"[]","has_code":false}