Nonlinear Control of Autonomous Flying Cars with Wings and Distributed Electric Propulsion

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 deeplearning-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 DNNbased 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.

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“…We assume that a nonlinear attitude controller uses the desired torque τ d from rotors to track R d (t). One such example is in [21]:…”

Section: A Reference Trajectory Trackingmentioning

confidence: 99%

Neural Lander: Stable Drone Landing Control Using Learned Dynamics

Shi

O’Connell

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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“…We also conduct experiment to show that the aircraft with its rotor orientations optimized can track attitude commands in all directions after two rotors fail, which is not the case when all rotors are simply vertical. For the experiment, we make use of a control allocation method recently proposed in [25], which aims to minimize energy consumption while trying to evenly distribute upward lift across all rotors to avoid potential saturation. The paper concludes in Sec.…”

Section: B Contribution and Paper Organizationmentioning

confidence: 99%

“…In the authors' previous work [25], we proposed another control allocation method that minimizes the energy consumption by optimizing the inverse E of E and reduces the risk of saturation by evenly distributing the effort of vertical thrust generation across all rotors:…”

Section: Control Allocationmentioning

confidence: 99%

Controllability and Design of Unmanned Multirotor Aircraft Robust to Rotor Failure

Kim

Rahili

Shi

et al. 2019

AIAA Scitech 2019 Forum

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A new design method for multi-rotor aircraft with distributed electric propulsion is presented to ensure a property of robustness against rotor failure from the control perspective. Based on the concept of null controllability, a quality measure is derived to evaluate and quantify the performance of a given design with the consideration of rotor failure. An optimization problem whose cost function is based on the quality measure is formulated and its optimal solution identifies a set of optimal design parameters that maximizes an aircraft's ability to control its attitude and hence its position. The effectiveness of the proposed design procedure is validated through the results of experimentation with the Autonomous Flying Ambulance model being developed at Caltech's Center for Autonomous Systems and Technologies.

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“…And execution speed can be critical for agile flyers. There has been some interest in designing a unified feedback controller for fixed-wing VTOL, such as in [10], [11], and our prior work [12]. The success of these controllers relies primarily on the accurate prediction of aerodynamic forces, which in turn requires high-fidelity models and accurate sensor feedback for states relevant to such forces.…”

Section: Introductionmentioning

confidence: 99%

“…Contribution and Organization: We build on the prior work in [12] where we proposed a general control architecture for fixed-wing VTOL. Here we focus on creating a velocity tracking controller with an adaptive force model and a novel 3D airflow sensor for accurate aerodynamic force prediction.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Nonlinear Control of Fixed-Wing VTOL with Airflow Vector Sensing

Shi

Spieler

Tang

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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Fixed-wing vertical takeoff and landing (VTOL) aircraft pose a unique control challenge that stems from complex aerodynamic interactions between wings and rotors. Thus, accurate estimation of external forces is indispensable for achieving high performance flight. In this paper, we present a composite adaptive nonlinear tracking controller for a fixedwing VTOL. The method employs online adaptation of linear force models, and generates accurate estimation for wing and rotor forces in real-time based on information from a threedimensional airflow sensor. The controller is implemented on a custom-built fixed-wing VTOL, which shows improved velocity tracking and force prediction during the transition stage from hover to forward flight, compared to baseline flight controllers.

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Nonlinear Control of Autonomous Flying Cars with Wings and Distributed Electric Propulsion

Cited by 26 publications

References 30 publications

Neural Lander: Stable Drone Landing Control Using Learned Dynamics

Neural Lander: Stable Drone Landing Control Using Learned Dynamics

Controllability and Design of Unmanned Multirotor Aircraft Robust to Rotor Failure

Adaptive Nonlinear Control of Fixed-Wing VTOL with Airflow Vector Sensing

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