Neural Lander: Stable Drone Landing Control using Learned Dynamics (1811.08027v2)

Abstract: Precise near-ground trajectory control is difficult for multi-rotor drones, due to the complex aerodynamic effects caused by interactions between multi-rotor airflow and the environment. Conventional control methods often fail to properly account for these complex effects and fall short in accomplishing smooth landing. In this paper, we present a novel deep-learning-based robust nonlinear controller (Neural Lander) that improves control performance of a quadrotor during landing. Our approach combines a nominal dynamics model with a Deep Neural Network (DNN) that learns high-order interactions. We apply spectral normalization (SN) to constrain the Lipschitz constant of the DNN. Leveraging this Lipschitz property, we design a nonlinear feedback linearization controller using the learned model and prove system stability with disturbance rejection. To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets. Experimental results demonstrate that the proposed controller significantly outperforms a Baseline Nonlinear Tracking Controller in both landing and cross-table trajectory tracking cases. We also empirically show that the DNN generalizes well to unseen data outside the training domain.

• The paper introduces Neural-Lander, a DNN-based nonlinear feedback controller that significantly reduces landing errors by learning complex dynamics with provable stability.
• It integrates spectral normalization to constrain the Lipschitz constant, enhancing robustness against dynamic disturbances and ensuring reliable control.
• Experimental results demonstrate vertical error reductions from 0.13m and 0.153m to near zero, underscoring the controller’s practical impact on quadrotor landing performance.

Neural Lander: A Deep Learning Approach to Enhanced Quadrotor Landing Control

The paper "Neural Lander: Stable Drone Landing Control Using Learned Dynamics" introduces a novel approach for controlling quadrotor drones during landing. The work addresses the persistent challenge of achieving precise near-ground trajectory control of multi-rotor drones, accounting for the complex interactions of multi-rotor airflow with the environment, commonly referred to as the ground effect.

Overview of the Approach

The proposed system, dubbed Neural-Lander, integrates a nominal dynamics model with a deep neural network (DNN) to form a robust nonlinear controller capable of improving the quadrotor's landing performance. This model captures and learns from the higher-order interactions often overlooked by traditional control approaches. A significant innovation introduced by the authors is the application of spectral normalization to constrain the Lipschitz constant in neural networks, thus ensuring the stability of the system in the face of dynamic disturbances.

The paper provides a rigorous theoretical framework, wherein the authors derive stability guarantees for their feedback linearization controller leveraging the learned model. This contribution is underscored by the first demonstration, to the best of the authors' knowledge, of a DNN-based nonlinear feedback controller with provable stability that can work with arbitrarily large neural networks.

Experimental Evaluation

In testing their model, the authors benchmark Neural-Lander against a baseline nonlinear tracking controller across several scenarios, including both landing and trajectory tracking over a table. Experimentation reveals that Neural-Lander significantly improves tracking performance:

• In landing scenarios, the Neural-Lander achieved a reduction in vertical axis error from 0.13 meters to zero, while also reducing lateral drifts by up to 90%.
• In trajectory tracking tasks, the decrease in vertical axis error was observed from 0.153 meters to 0.027 meters.

The extensive empirical analysis also demonstrates the DNN's capability to generalize well to unseen data, a property that is notably enhanced by spectral normalization.

Implications and Future Directions

This research extends beyond practical implementations in autonomous flight by augmenting traditional control methods with advanced AI principles. The theoretical stability results are particularly noteworthy and contribute to the broader discourse on the use of deep learning in safety-critical applications. The intersection of spectral normalization with stability guarantees is an insightful domain worth further exploration.

Future investigations could extend the Neural-Lander framework to handle more diverse environmental interactions, such as with wind disturbances and more complex terrains. Such efforts could be directed towards broadening the applicability of this approach to various UAV configurations and broader autonomous aerial tasks. Ultimately, the paper emphasizes the potential of integrating learned dynamics into robust control algorithms, a focus with significant implications for advancing autonomous system capabilities.