Evaluation of an L1 Adaptive Flight Control Law on Calspan’s Variable-Stability Learjet

Ackerman, Kasey A.; Xargay, Enric; Choe, Ronald; Hovakimyan, Naira; Cotting, M. Christopher; Jeffrey, Robert B.; Blackstun, Margaret P.; Fulkerson, Timothy P.; Lau, Timothy R.; Stephens, Shawn S.

doi:10.2514/1.g001730

Cited by 36 publications

(16 citation statements)

References 7 publications

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“…where 𝑥 𝑓 2 (𝑡) ∈ R 𝑛 𝑓 and 𝑥 𝐼 2 (𝑡) ∈ R 𝑚 are the states of 𝐷 0 (𝑠) and the integrator in (9), respectively, and…”

Section: Transient and Steady-state Performancementioning

confidence: 99%

“…Benefiting from the fast and robust adaptation, L 1 AC provides guaranteed tracking performance, both in transient and steady-state, and robustness (e.g., nonzero time delay margin), in the presence of various uncertainties and disturbances [4]. We note that L 1 AC has been verified on a large number of real-world systems [4,6], including NASA's subscale commercial jet [7,8], and Calspan's Learjet (a piloted aircraft) [9,10]. When incorporating the adaptive control scheme into the L2F framework, we need to address a crucial issue, i.e., updating the reference model as the data collection and model learning processes evolve.…”

Section: Introductionmentioning

confidence: 99%

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Online Control Design for Learn-to-Fly

Snyder

Bacony

Morelli

et al. 2018

2018 Atmospheric Flight Mechanics Conference

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Two methods were developed for online control design as part of a flight test effort to examine the feasibility of the NASA Learn-to-Fly concept. The methods use an aerodynamic model of the aircraft that is being identified in real-time onboard the aircraft to adjust the control parameters. One method employs adaptive nonlinear dynamic inversion, whereas the other consists of a classical autopilot structure. Effects from the interaction between the realtime modeling and the developed control laws are discussed. The Learn-to-Fly concept has been deemed feasible based on successful flights of both a stable and unstable aircraft.= wing span c = wing mean aerodynamic chord C l , C m , C n = body-axis nondimensional aerodynamic moment coefficients g = acceleration due to gravity I x x , I y y , I z z , I x z = inertia tensor elements J = advance ratio K = control gain L = aerodynamic lift m = mass M = vector of aerodynamic moments M δ = vector of aerodynamic moments due to control surface deflection n c = number of modeling regressors N = number of data points p, q, r = body-axis roll, pitch, and yaw rates q = dynamic pressure R = propeller radius S = wing reference area T x , T z = body x-axis and body z-axis components of engine thrust T h = throttle V = true airspeed V C AS = calibrated airspeed X = matrix of modeling regressors Y = aerodynamic side force z = vector of observations α = angle of attack β = sideslip angle δ = surface deflection ε = vector of residuals * Research Engineer, Dynamic Systems and Controls Branch, MS 308

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“…where 𝑥 𝑓 2 (𝑡) ∈ R 𝑛 𝑓 and 𝑥 𝐼 2 (𝑡) ∈ R 𝑚 are the states of 𝐷 0 (𝑠) and the integrator in (9), respectively, and…”

Section: Transient and Steady-state Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Control Design for Learn-to-Fly

Snyder

Bacony

Morelli

et al. 2018

2018 Atmospheric Flight Mechanics Conference

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“…A critical property of the L 1 control enabling the fast adaptation is that the estimation is decoupled from control; thus, the arbitrarily fast adaptation does not destroy the robustness of the closed-loop system [25]. The L 1 adaptive control has been successfully validated on NASA's AirStar 5.5% subscale generic transport aircraft model [26], [27], Calspan's Learjet [28], [29], and unmanned aerial vehicles [30]- [32]. The L 1 adaptive controller design for quadrotors has been studied in [33], [34], where Euler angles are used to model the dynamics.…”

Section: Introductionmentioning

confidence: 99%

$\mathcal{L}_1$ Adaptive Augmentation for Geometric Tracking Control of Quadrotors

Wu¹,

Cheng²,

Ackerman³

et al. 2021

Preprint

Self Cite

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This paper introduces an L1 adaptive control augmentation for geometric tracking control of quadrotors. In the proposed design, the L1 augmentation handles nonlinear (timeand state-dependent) uncertainties in the quadrotor dynamics without assuming/enforcing parametric structures, while the baseline geometric controller achieves stabilization of the known nonlinear model of the system dynamics. The L1 augmentation applies to both the rotational and the translational dynamics. Experimental results demonstrate that the augmented geometric controller shows consistent and (on average five times) smaller trajectory tracking errors compared with the geometric controller alone when tested for different trajectories and under various types of uncertainties/disturbances.

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“…In the context of aerospace applications, the experimental validation of L 1 controllers has been extensively reported in refs. [34][35][36][37]. To the authors' knowledge, the effort reported in this paper is the first to seek to achieve such validation for manipulators on ground-based dynamic platforms.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of an Adaptive Controller for Manipulators on a Dynamic Platform

Reina

Nguyen

Dankowicz³

2020

Robotica

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SUMMARY This paper reports on laboratory and field experimental results for controlled robotic manipulators operating on moving platforms with unmodeled dynamics. The aim is to validate theoretical predictions for the dependence on control parameters of an adaptive control strategy. In addition, the results provide insight into different discretizations of the continuous-time formulation, suggesting the most suitable discretization scheme for hardware implementation. The second set of experimental results, obtained from an implementation of the control framework for synchronization and consensus in networks of robotic manipulators, similarly validate theoretical predictions on the sensitivity to network communication delays.

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Evaluation of an L1 Adaptive Flight Control Law on Calspan’s Variable-Stability Learjet

Cited by 36 publications

References 7 publications

Online Control Design for Learn-to-Fly

Online Control Design for Learn-to-Fly

$\mathcal{L}_1$ Adaptive Augmentation for Geometric Tracking Control of Quadrotors

Experimental Validation of an Adaptive Controller for Manipulators on a Dynamic Platform

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