L<sub>1</sub>Adaptive Augmentation for Geometric Tracking Control of Quadrotors

Wu, Zhuohuan; Cheng, Sheng; Ackerman, Kasey A.; Gahlawat, Aditya; Lakshmanan, Arun; Zhao, Pan; Hovakimyan, Naira

doi:10.1109/icra46639.2022.9811946

Cited by 15 publications

(3 citation statements)

References 38 publications

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“…This article is an extension of our previous conference paper [32], in which we show preliminary results experimented on a Parrot Mambo quadrotor. In this article, we additionally establish performance guarantees for the proposed controller with tunable bounds.…”

Section: Introductionmentioning

confidence: 84%

$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees

Wu¹,

Cheng²,

Zhao³

et al. 2023

Preprint

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Quadrotors that can operate safely in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications. We present L1Quad, a control architecture for quadrotors based on the L1 adaptive control. L1Quad enables safe tubes centered around a desired trajectory that the quadrotor is always guaranteed to remain inside. Our design applies to both the rotational and the translational dynamics of the quadrotor. We lump various types of uncertainties and disturbances as unknown nonlinear (timeand state-dependent) forces and moments. Without assuming or enforcing parametric structures, L1Quad can accurately estimate and compensate for these unknown forces and moments. Extensive experimental results demonstrate that L1Quad is able to significantly outperform baseline controllers under a variety of uncertainties with consistently small tracking errors.

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Section: Introductionmentioning

confidence: 84%

$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees

Wu¹,

Cheng²,

Zhao³

et al. 2023

Preprint

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“…We suspected that these oscillations might be due to the inadequate adaptability of the L 1 adaptive controller. Additionally, we observed deviations between the actual and reference states in several papers [24,30,31]. Thus, we examined the L1 controller and eventually identified that the L 1 -Pc control system still has two limitations that remain unresolved.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, a growing number of scholars have devoted themselves to theoretical research [21][22][23][24], the evaluation [25][26][27] and the application [28][29][30][31][32] of L 1 -Pc. Jintasit [29] combined model-predictive path integral control with L 1 adaptive control to achieve effective trajectory tracking for quad-rotor aircraft.…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Adaptive Autopilot for Unmanned Aerial Vehicles Based on the Extension of Regression Matrix

Feng

et al. 2023

Drones

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In applications of the L1 adaptive flight control system, we found two limitations to be extended: (1) the system cannot meet the demands of engineering in terms of nonlinearity and adaptation in most flight scenarios; (2) the adaptive control law generates a transient response in the tracking error, hindering the system from reaching the steady-state error, and ultimately decreasing control accuracy. In response to these problems, an extended flight control system for L1 adaptive theory is proposed and rigorously proved. This system involves considering the nonlinear function matrix of state variables, which serves as an extension of the regression matrix in the original L1 adaptive control system, thus enhancing its nonlinear characteristics. The problem of calculating the adaptive laws, caused by the extended regression matrix, is solved by using the pseudo-inverse matrix. To eliminate the transient response, the state vector and its estimate are recorded and employed just like an integrator. Finally, the proposed system is verified on a high-subsonic flight subject to nonlinear uncertainties, with simulation results showing improved control accuracy and enhanced robustness. The proposed system resolves the limitations of the L1 adaptive control system in nonlinearity, providing the possibility for further theoretical development to improve the performance of adaptive control systems.

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State predictor-based deep model reference adaptive control for quadrotor trajectory tracking

Cheng,

Yang,

Sun

et al. 2025

Aerospace Science and Technology

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L₁Adaptive Augmentation for Geometric Tracking Control of Quadrotors

Cited by 15 publications

References 38 publications

$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees

$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees

A Nonlinear Adaptive Autopilot for Unmanned Aerial Vehicles Based on the Extension of Regression Matrix

State predictor-based deep model reference adaptive control for quadrotor trajectory tracking

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L1Adaptive Augmentation for Geometric Tracking Control of Quadrotors

Cited by 15 publications

References 38 publications

$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees

$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees

A Nonlinear Adaptive Autopilot for Unmanned Aerial Vehicles Based on the Extension of Regression Matrix

State predictor-based deep model reference adaptive control for quadrotor trajectory tracking

Contact Info

Product

Resources

About

L₁Adaptive Augmentation for Geometric Tracking Control of Quadrotors