A unified approach to fixed-wing aircraft path following guidance and control

Kai, Jean-Marie; Hamel, Tarek; Samson, Claude

doi:10.1016/j.automatica.2019.07.004

Cited by 27 publications

(15 citation statements)

References 20 publications

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“…Therefore, future work could consider different robust and/or adaptive control techniques to better compensate for a wider class of exogenous disturbances, including harsh wind conditions. Also, the coordinated-turn equation, Equation (20), is only an approximation of the relationship between roll angle and turn rate. Future work could seek to relax the assumptions used to derive this relation, e.g., by considering the equations derived in [41], and do a comparative study of different turn coordination methods for fixed-wing UAVs.…”

Section: Discussionmentioning

confidence: 99%

“…In [19], path following is achieved by using an existing commercial inner-loop autopilot but augmented with an L 1 adaptive controller to deal with modeling uncertainty and environmental disturbances. While most guidance algorithms use only kinematic models, an integrated approach is presented in [20] that uses a simple model of the aerodynamic forces acting on the aircraft. Common to all the mentioned approaches, both IGC and SGC, is the reliance on attitude control in the inner-most loop.…”

Section: Introduction 1background and Motivationmentioning

confidence: 99%

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Geometric Reduced-Attitude Control of Fixed-Wing UAVs

Coates

Fossen

2021

Applied Sciences

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This paper presents nonlinear, singularity-free autopilot designs for multivariable reduced-attitude control of fixed-wing aircraft. To control roll and pitch angles, we employ vector coordinates constrained to the unit two-sphere and that are independent of the yaw/heading angle. The angular velocity projected onto this vector is enforced to satisfy the coordinated-turn equation. We exploit model structure in the design and prove almost global asymptotic stability using Lyapunov-based tools. Slowly-varying aerodynamic disturbances are compensated for using adaptive backstepping. To emphasize the practical application of our result, we also establish the ultimate boundedness of the solutions under a simplified controller that only depends on rough estimates of the control-effectiveness matrix. The controller design can be used with state-of-the-art guidance systems for fixed-wing unmanned aerial vehicles (UAVs) and is implemented in the open-source autopilot ArduPilot for validation through realistic software-in-the-loop (SITL) simulations.

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Section: Discussionmentioning

confidence: 99%

Section: Introduction 1background and Motivationmentioning

confidence: 99%

Geometric Reduced-Attitude Control of Fixed-Wing UAVs

Coates

Fossen

2021

Applied Sciences

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“…They consider fixed-wing UAVs that generate their forward speed using a propulsion system such as propellers driven by electric motors or gas engines. A more detailed exploration of path following for fixed-wing aircraft can be found in [6]. Here, the authors provide control solutions based on theoretical stability and convergence analyses, which can be applied to almost all regular 3D paths.…”

Section: Related Work a Aircraft Guidance Navigation And Controlmentioning

confidence: 99%

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Miraglia

Hook

2021

IEEE Access

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New generations of small advanced aircraft may soon transform how people and freight are transported. Thus, ensuring safe and reliable operation of these systems, in all plausible scenarios, is of utmost priority. In this paper, we address this problem by proposing a method to verify the safety of an autonomous aircraft controller following a navigational policy. The policy is encoded using discrete vector fields that are meant to drive the vehicle to a goal state and away from obstacles or restricted airspace. The solution presented in this paper provides the ability to verify the policy, catching unsafe scenarios which may be missed by random Monte Carlo methods or general reachability analysis. After illustrating the main theoretical principles, a possible practical implementation is described and compared with analysis based on Monte Carlo simulations. The comparison shows an insightful example where Monte Carlo simulations fail to detect several corner cases that are uncovered by the proposed method. In the conclusion of the paper, we provide insights about possible future research to implement the proposed solution obtaining higher accuracy and efficiency. INDEX TERMS Navigation, Unmanned aerial vehicles, Autonomous agents, Reachability analysis VOLUME 4, 2016 This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2021.3062662, IEEE Access Giovanni Miraglia et al.: Geometric Verification of Vector Fields for Autonomous Aircraft Navigation Giovanni Miraglia et al.: Geometric Verification of Vector Fields for Autonomous Aircraft Navigation LOYD R. HOOK (Member, IEEE) has worked in aircraft autonomous systems research and implementation for the past 15 years beginning at NASA Dryden/Armstrong flight research center and continuing at his current position as Assistant Professor at the University of Tulsa. Loyd is currently the director of the TU Vehicle Autonomy and Intelligence Lab (TU VAIL) where his research focus is the development of safety assured autonomous vehicle systems. Loyd has MS and Ph.D. degrees from the University of Oklahoma in Electrical and Computer Engineering from 2006 and 2011 respectively.

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“…T HE pose motion control of the rigid body is a hot topic in the automation field for many years, since it is the physical nature of many kinds of man-made moving bodies in practice, such as the motions of automotive vehicles [1], aircrafts [2], spacecraft [3], marine vessels [4] and formations [5]. In particular, the relative motion between two vehicles is very common in advanced modern industry, such as the air refueling [6], shipboard landing [7], space docking [8] and underwater docking [9].…”

Section: Introductionmentioning

confidence: 99%

Robust Hierarchical Adaptive Fuzzy Relative Motion Coordination for Feature Points of Two Rigid Bodies With Input and Output Constraints

Sun

Jiang

2022

IEEE Trans. Fuzzy Syst.

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Jingjing Jiang received the B.E. Degree in Electrical and Electronic Engineering from the University of Birmingham, UK, and the Harbin Institute of Technology, China, in 2010, the M.Sc. degree in Control Systems from Imperial College London, UK, in 2011, and the Ph.D degree from Imperial College London, in 2016. She carried out research as part of the Control and Power Group at Imperial College and joined Loughborough University as a Lecturer in September 2018. Her current research interests include driver assistance control and autonomous vehicle control design, control design of systems with constraints and human-in-the-loop.

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A unified approach to fixed-wing aircraft path following guidance and control

Cited by 27 publications

References 20 publications

Geometric Reduced-Attitude Control of Fixed-Wing UAVs

Geometric Reduced-Attitude Control of Fixed-Wing UAVs

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Robust Hierarchical Adaptive Fuzzy Relative Motion Coordination for Feature Points of Two Rigid Bodies With Input and Output Constraints

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