# Learning-based vs Model-free Adaptive Control of a MAV under Wind Gust

arXiv: https://arxiv.org/abs/2101.12501 · HTML: https://mlsanity.com/paper/2101.12501/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 66 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [A Deeper Look at Experience Replay](https://mlsanity.com/paper/1712.01275/) (2017)
- [The Limits and Potentials of Deep Learning for Robotics](https://mlsanity.com/paper/1804.06557/) (2018)
- [Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation](https://mlsanity.com/paper/1709.10489/) (2017)
- [An empirical investigation of the challenges of real-world reinforcement learning](https://mlsanity.com/paper/2003.11881/) (2020)
- [Sim-to-Real Transfer with Incremental Environment Complexity for Reinforcement Learning of Depth-Based Robot Navigation](https://mlsanity.com/paper/2004.14684/) (2020)
- [Geometric Adaptive Control with Neural Networks for a Quadrotor UAV in Wind fields](https://mlsanity.com/paper/1903.02091/) (2019)
- [Geometric Adaptive Control for a Quadrotor UAV with Wind Disturbance Rejection](https://mlsanity.com/paper/1803.06363/) (2018)
- [H2 Optimized PID Control of Quad-Copter Platform with Wind Disturbance](https://mlsanity.com/paper/2003.13801/) (2020)
- [Safe Learning-based Tracking Control for Quadrotors under Wind Disturbances](https://mlsanity.com/paper/2009.01992/) (2020)

## Built on by (most cited first, 2 of 2)

- [Sim-to-Real Transfer of Adaptive Control Parameters for AUV Stabilization under Current Disturbance](https://mlsanity.com/paper/2310.11075/) (2023)
- [PID Tuning using Cross-Entropy Deep Learning: a Lyapunov Stability Analysis](https://mlsanity.com/paper/2404.12025/) (2024)

## Abstract (arXiv)

Navigation problems under unknown varying conditions are among the most important and well-studied problems in the control field. Classic model-based adaptive control methods can be applied only when a convenient model of the plant or environment is provided. Recent model-free adaptive control methods aim at removing this dependency by learning the physical characteristics of the plant and/or process directly from sensor feedback. Although there have been prior attempts at improving these techniques, it remains an open question as to whether it is possible to cope with real-world uncertainties in a control system that is fully based on either paradigm. We propose a conceptually simple learning-based approach composed of a full state feedback controller, tuned robustly by a deep reinforcement learning framework based on the Soft Actor-Critic algorithm. We compare it, in realistic simulations, to a model-free controller that uses the same deep reinforcement learning framework for the control of a micro aerial vehicle under wind gust. The results indicate the great potential of learning-based adaptive control methods in modern dynamical systems.