Learned Incremental Nonlinear Dynamic Inversion for Quadrotors with and without Slung Payloads

Eckart Cobo-Briesewitz, Khaled Wahba, Wolfgang Hönig
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:1260-1274, 2026.

Abstract

The increasing complexity of multirotor applications demands flight controllers that can accurately account for all forces acting on the vehicle. Conventional controllers model most aerodynamic and dynamic effects but often neglect higher-order forces, as their accurate estimation is computationally expensive. Incremental Nonlinear Dynamic Inversion (INDI) offers an alternative by estimating residual forces from differences in sensor measurements; however, its reliance on specialized and often noisy sensors limits its applicability. Recent work has demonstrated that residual forces can be predicted using learning-based methods. In this paper, we show that a neural network can generate smooth approximations of INDI outputs without requiring additional sensor inputs. We further propose a hybrid approach that integrates learning-based predictions with INDI and demonstrate both methods for multirotors and multirotors carrying slung payloads. Experimental results on trajectory tracking errors demonstrate that the specialized sensor measurements required by INDI can be eliminated by replacing the residual computation with a neural network.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-cobo-briesewitz26a, title = {Learned Incremental Nonlinear Dynamic Inversion for Quadrotors with and without Slung Payloads}, author = {Cobo-Briesewitz, Eckart and Wahba, Khaled and H\"onig, Wolfgang}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {1260--1274}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/cobo-briesewitz26a/cobo-briesewitz26a.pdf}, url = {https://proceedings.mlr.press/v331/cobo-briesewitz26a.html}, abstract = {The increasing complexity of multirotor applications demands flight controllers that can accurately account for all forces acting on the vehicle. Conventional controllers model most aerodynamic and dynamic effects but often neglect higher-order forces, as their accurate estimation is computationally expensive. Incremental Nonlinear Dynamic Inversion (INDI) offers an alternative by estimating residual forces from differences in sensor measurements; however, its reliance on specialized and often noisy sensors limits its applicability. Recent work has demonstrated that residual forces can be predicted using learning-based methods. In this paper, we show that a neural network can generate smooth approximations of INDI outputs without requiring additional sensor inputs. We further propose a hybrid approach that integrates learning-based predictions with INDI and demonstrate both methods for multirotors and multirotors carrying slung payloads. Experimental results on trajectory tracking errors demonstrate that the specialized sensor measurements required by INDI can be eliminated by replacing the residual computation with a neural network.} }
Endnote
%0 Conference Paper %T Learned Incremental Nonlinear Dynamic Inversion for Quadrotors with and without Slung Payloads %A Eckart Cobo-Briesewitz %A Khaled Wahba %A Wolfgang Hönig %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-cobo-briesewitz26a %I PMLR %P 1260--1274 %U https://proceedings.mlr.press/v331/cobo-briesewitz26a.html %V 331 %X The increasing complexity of multirotor applications demands flight controllers that can accurately account for all forces acting on the vehicle. Conventional controllers model most aerodynamic and dynamic effects but often neglect higher-order forces, as their accurate estimation is computationally expensive. Incremental Nonlinear Dynamic Inversion (INDI) offers an alternative by estimating residual forces from differences in sensor measurements; however, its reliance on specialized and often noisy sensors limits its applicability. Recent work has demonstrated that residual forces can be predicted using learning-based methods. In this paper, we show that a neural network can generate smooth approximations of INDI outputs without requiring additional sensor inputs. We further propose a hybrid approach that integrates learning-based predictions with INDI and demonstrate both methods for multirotors and multirotors carrying slung payloads. Experimental results on trajectory tracking errors demonstrate that the specialized sensor measurements required by INDI can be eliminated by replacing the residual computation with a neural network.
APA
Cobo-Briesewitz, E., Wahba, K. & Hönig, W.. (2026). Learned Incremental Nonlinear Dynamic Inversion for Quadrotors with and without Slung Payloads. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:1260-1274 Available from https://proceedings.mlr.press/v331/cobo-briesewitz26a.html.

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