mlsanityData to 7 Oct 2026

Data-Driven MPC for Quadrotors

arXiv 2102.05773 ↗First posted 10 Feb 2021cs.ROcited by 32

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Abstract (arXiv)

Aerodynamic forces render accurate high-speed trajectory tracking with quadrotors extremely challenging. These complex aerodynamic effects become a significant disturbance at high speeds, introducing large positional tracking errors, and are extremely difficult to model. To fly at high speeds, feedback control must be able to account for these aerodynamic effects in real-time. This necessitates a modelling procedure that is both accurate and efficient to evaluate. Therefore, we present an approach to model aerodynamic effects using Gaussian Processes, which we incorporate into a Model Predictive Controller to achieve efficient and precise real-time feedback control, leading to up to 70% reduction in trajectory tracking error at high speeds. We verify our method by extensive comparison to a state-of-the-art linear drag model in synthetic and real-world experiments at speeds of up to 14m/s and accelerations beyond 4g.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Feb 2021.

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Citing papers per year: 2021: 2; 2022: 14; 2023: 4; 2024: 7; 2025: 5.

32 papers.

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  1. 2022Neural-Fly Enables Rapid Learning for Agile Flight in Strong Windscited by 80
  2. 2022Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platformscited by 36
  3. 2024VLMPC: Vision-Language Model Predictive Control for Robotic Manipulationcited by 32
  4. 2021KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robotscited by 26
  5. 2022Online Dynamics Learning for Predictive Control with an Application to Aerial Robotscited by 17
  6. 2022Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Trackingcited by 16
  7. 2022Learning a Single Near-hover Position Controller for Vastly Different Quadcopterscited by 16
  8. 2021Evaluating model-based planning and planner amortization for continuous controlcited by 15
  9. 2022Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Controlcited by 13
  10. 2022RAMP-Net: A Robust Adaptive MPC for Quadrotors via Physics-informed Neural Networkcited by 12
  11. 2023DATT: Deep Adaptive Trajectory Tracking for Quadrotor Controlcited by 11
  12. 2024Feedback Favors the Generalization of Neural ODEscited by 8
20 more
  1. 2022VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terraincited by 7
  2. 2024DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Modelscited by 6
  3. 2023Constrained Reinforcement Learning using Distributional Representation for Trustworthy Quadrotor UAV Tracking Controlcited by 5
  4. 2022Neural Solvers for Fast and Accurate Numerical Optimal Controlcited by 4
  5. 2022Zero-Order Optimization for Gaussian Process-based Model Predictive Controlcited by 4
  6. 2025Vision-Language Model Predictive Control for Manipulation Planning and Trajectory Generationcited by 4
  7. 2024AI-Aided Kalman Filterscited by 3
  8. 2023Learning the References of Online Model Predictive Control for Urban Self-Drivingcited by 2
  9. 2022Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systemscited by 1
  10. 2022Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learningcited by 1
  11. 2023Differentially Flat Learning-based Model Predictive Control Using a Stability, State, and Input Constraining Safety Filtercited by 1
  12. 2024Why Change Your Controller When You Can Change Your Planner: Drag-Aware Trajectory Generation for Quadrotor Systemscited by 1
  13. 2024Learning Long-Horizon Predictions for Quadrotor Dynamicscited by 1
  14. 2025A New Perspective On AI Safety Through Control Theory Methodologiescited by 1
  15. 2025PI-WAN: A Physics-Informed Wind-Adaptive Network for Quadrotor Dynamics Prediction in Unknown Environmentscited by 1
  16. 2022LEARNEST: LEARNing Enhanced Model-based State ESTimation for Robots using Knowledge-based Neural Ordinary Differential Equationscited by 0
  17. 2022Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensemblescited by 0
  18. 2024Proto-MPC: An Encoder-Prototype-Decoder Approach for Quadrotor Control in Challenging Windscited by 0
  19. 2025A robust and adaptive MPC formulation for Gaussian process modelscited by 0
  20. 2025Dyna-Style Reinforcement Learning Modeling and Control of Non-linear Dynamicscited by 0