Model Predictive Control of a Convertible Tiltrotor Unmanned Aerial Vehicle

Allenspach, Mike; Ducard, Guillaume

doi:10.1109/med48518.2020.9183353

Cited by 12 publications

(6 citation statements)

References 10 publications

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“…This abstraction reduces the aerodynamic modeling required for the NMPC to the translational dynamics. The reduced dimensionality and non-linearity of the problem is important to enable real-time RHC, specifically also in light of the increased feedback rates (>50 Hz) that would be required for attitude stabilization in a unified framework [9].…”

Section: System Modelingmentioning

confidence: 99%

“…The use of the presented model within gradient-based trajectory optimization methods requires a careful analysis of the state and control Jacobians ∂ r In ( 8), (9) the limits (u i , w i ) → (0, 0) of the Jacobian ∂F (x,z),i /∂x require particular attention. While the forces F (x,z),i are continuously differentiable at the origin, the arctan2(0, 0) itself in (11) is not even defined.…”

Section: B Implementation Considerationsmentioning

confidence: 99%

“…For the two model structures defined in II, the respective parameters and aerodynamic coefficients p are estimated from the collected flight data and optimized for predictive performance. To this end, we minimize the errors e v of the open-loop simulated velocities (8), (9) together with linear and angular acceleration errors e a , e q in a least squares sense…”

Section: B Parameter Estimationmentioning

confidence: 99%

“…For tilt rotor designs, a few examples apply (N)MPC in real flight experiments, e.g. [9]. For the standard flight envelope of a tiltrotor, however, modeling of stalled airfoils is not necessary and often, propeller-wing interaction is omitted by design.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Nonlinear Model Predictive Velocity Control of a VTOL Tiltwing UAV

Rohr

Studiger

Stastny

et al. 2021

IEEE Robot. Autom. Lett.

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This letter presents the modeling, system identification and nonlinear model predictive control (NMPC) design for longitudinal, full envelope velocity control of a small tiltwing hybrid unmanned aerial vehicle (H-UAV). A first-principles based dynamics model is derived and identified from flight data. It captures important aerodynamic effects including propeller-wing interaction and stalled airfoils, but is still simple enough for on-board online trajectory optimization. Based on this model, a high-level NMPC is formulated which optimizes throttle, tilt-rate and pitch-angle setpoints in order to track longitudinal velocity trajectories. We propose and investigate different references suitable to regularize the optimization problem, including both offline generated trims as well as preceding NMPC solutions. In simulation, we compare the NMPC with a frequently reported dynamic inversion approach for H-UAV velocity control. Finally, the NMPC is validated in flight experiments through a series of transition maneuvers, demonstrating good tracking capabilities in the full flight envelope.

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Section: System Modelingmentioning

confidence: 99%

Section: B Implementation Considerationsmentioning

confidence: 99%

Section: B Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nonlinear Model Predictive Velocity Control of a VTOL Tiltwing UAV

Rohr

Studiger

Stastny

et al. 2021

IEEE Robot. Autom. Lett.

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“…In general, one can say that convertible UAV's control is a current area of research since current and further applications this, relatively new, aircraft can perform like inspection of static objects in a big area. Tough the investigation of controls for convertible UAVs has been some success during the last years, it remains to tackle this problem from a practical point of view since the vast majority of the works are focused only on the theoretical part [1] [12][5][4] [13]. However, such investigation has demonstrated that one of the key points for controlling convertible aircraft is the lack of knowledge of the aerodynamic forces, especially during transition phases.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a neural network for non-linearities estimation in a tail-sitter aircraft

Flores¹,

Flores²

2020

Preprint

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The control of a tail-sitter aircraft is a challenging task, especially during transition maneuver where the lift and drag forces are highly nonlinear. In this work, we implement a Neural Network (NN) capable of estimate such nonlinearities. Once they are estimated, one can propose a control scheme where these forces can correctly feed-forwarded. Our implementation of the NN has been programmed in C++ on the PX4 Autopilot an open-source autopilot for drones. To ensure that this implementation does not considerably affect the autopilot's performance, the coded NN must be of a light computational load. With the aim to test our approach, we have carried out a series of realistic simulations in the Software in The Loop (SITL) using the PX4 Autopilot. These experiments demonstrate that the implemented NN can be used to estimate the tail-sitter aerodynamic forces, and can be used to improve the control algorithms during all the flight phases of the tail-sitter aircraft: hover, cruise flight, and transition.

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Implementation of a Neural Network for Nonlinearities Estimation in a Tail-Sitter Aircraft

Flores

2021

J Intell Robot Syst

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Model Predictive Control of a Convertible Tiltrotor Unmanned Aerial Vehicle

Cited by 12 publications

References 10 publications

Nonlinear Model Predictive Velocity Control of a VTOL Tiltwing UAV

Nonlinear Model Predictive Velocity Control of a VTOL Tiltwing UAV

Implementation of a neural network for non-linearities estimation in a tail-sitter aircraft

Implementation of a Neural Network for Nonlinearities Estimation in a Tail-Sitter Aircraft

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