Nonlinear Model Predictive Guidance for Fixed-wing UAVs Using Identified Control Augmented Dynamics

Stastny, Thomas; Siegwart, Roland

doi:10.1109/icuas.2018.8453377

Cited by 37 publications

(20 citation statements)

References 24 publications

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“…In words, the vehicle should converge to the path while heading in the correct direction. Similar to [9], we define a purely track-error based lookahead vectorl:l = cos θ lê + sin θ ltP (10) whereê = e/ e , e = 0 is the unit track-error and lookahead angle θ l = f (e) ∈ 0, π 2 maps track-offset to a reference angle of approach to the path.…”

Section: A Baseline Path Followingmentioning

confidence: 99%

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On Flying Backwards: Preventing Run-away of Small, Low-speed, Fixed-wing UAVs in Strong Winds

Stastny

Siegwart

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Small, low-speed fixed-wing Unmanned Aerial Vehicles (UAVs) operating autonomously, beyond-visual-lineof-sight (BVLOS) will inevitably encounter winds rising to levels near or exceeding the vehicles' nominal airspeed. In this paper, we develop a nonlinear lateral-directional path following guidance law with explicit consideration of online wind estimates. Energy efficient airspeed reference compensation logic is developed for excess wind scenarios (i.e. when the wind speed rises above the airspeed), enabling either mitigation, prevention, or over-powering of excess wind induced run-away from a given path. The developed guidance law is demonstrated on a representative small, low-speed test UAV in two flight experiments conducted in mountainous regions of Switzerland with strong, turbulent wind conditions, gusts reaching up to 13 meters per second. We demonstrate trackkeeping errors of less than 1 meter consistently maintained during a representative duration of gusting, excess winds and a mean ground speed undershoot of 0.5 meters per second from the commanded minimum forward ground speed demonstrated in over 5 minutes of the showcased flight results.

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Section: A Baseline Path Followingmentioning

confidence: 99%

“…Similar to the guidance augmentation in [10], we extend the formulations in [8], [9] with an adaptive track error boundary e b , taking into account the current ground speed v G = v G :…”

Section: A Baseline Path Followingmentioning

confidence: 99%

On Flying Backwards: Preventing Run-away of Small, Low-speed, Fixed-wing UAVs in Strong Winds

Stastny

Siegwart

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Furthermore, such control systems need to respect flight envelope boundaries to protect the aircraft from entering stall. Low-fidelity approaches such as total energy control (TECS) [2] implemented in the PX4 autopilot 1 , modelbased nonlinear dynamic inversion (NDI) [3], [4] as well as full receding horizon optimal control approaches (RHC) [5], [6] are reported, with the latter two being applicable to both fixed-and rotary-wing UAVs. By optimizing over the forward simulated system response, receding horizon control approaches offer the advantage to explicitly deal with actuator-and state constraints that become relevant when flying highly dynamic maneuvers and to ensure flight envelope protection.…”

Section: Introductionmentioning

confidence: 99%

“…2) Contribution: First, we develop and identify a firstprinciples based model from flight data that captures the main aerodynamics, including stalled airfoils and the propeller-wing interaction, but is still lightweight enough for RHC. Following [6], an attitude control augmented formulation is adopted to reduce model complexity and to limit aerodynamic modeling to the translational dynamics. To model involved forces, we consider elements proposed in [1] as well as an alternative formulation for the forces of the propeller-wing interaction.…”

Section: Introductionmentioning

confidence: 99%

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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“…The fixed wing structural design has been preferred as it is tranquil to preserve than a unadventurous aircraft, and supplementary strong in circumstance of devastating. Customarily the tail agonize the wickedest portion, as the association to the central frame is fragile [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Stability of Digital Control System for Unmanned Aerial Vehicle with Numerical Analysis

Win¹,

Nyunt²,

Tun³

2020

AJAA

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The paper presents the analysis on stability of digital control system for unmanned vehicle with numerical analysis. The objective of this study is mainly emphasized on the fulfillment of the advanced control techniques according to the fundamental concepts of digital control system approaches for unmanned aerial vehicle system design. The targeted unmanned aerial vehicle system was designed based on the simple construction under the idea of fixed wing flight system approaches. The stability analyses on unmanned aerial vehicle are vital role to enhance the real world applications. The background concepts on digital control system for stability analysis on dynamic control system like unmanned aerial vehicle system. The appropriate controller design for dynamic control system of unmanned aerial vehicle is vital role to analyze the accurate stability condition for reality. The implementation of numerical analysis on compensator design has been developed by using MATLAB. The physical parameters in these analyses are based on the experimental outcomes from the recent research works in dynamical system implementations. The mathematical approaches are very helpful for numerical analysis on compensator design for the unmanned aerial vehicles schemes. The simulation results confirm that the high performance stability test on unmanned aerial vehicle system has been met with the theoretical works.

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Nonlinear Model Predictive Guidance for Fixed-wing UAVs Using Identified Control Augmented Dynamics

Cited by 37 publications

References 24 publications

On Flying Backwards: Preventing Run-away of Small, Low-speed, Fixed-wing UAVs in Strong Winds

On Flying Backwards: Preventing Run-away of Small, Low-speed, Fixed-wing UAVs in Strong Winds

Nonlinear Model Predictive Velocity Control of a VTOL Tiltwing UAV

Stability of Digital Control System for Unmanned Aerial Vehicle with Numerical Analysis

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