L1 Stability Augmentation System for 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:10.2514/6.2016-0631

Cited by 32 publications

(13 citation statements)

References 5 publications

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“…Among the various adaptive control methods, L 1 adaptive control [5], [6] has been widely adopted due to its attractive properties of fast adaption, guaranteed robustness, and predictable transient response. These properties have been verified -consistently with the theory -in multiple manned and unmanned flight tests and simulations, on a diverse array of aircraft, including fixed-wing aircraft [7]- [13], multirotors [14]- [16], helicopters [17], and air-defense missiles [18]. The L 1 adaptive control law can be formulated either as a standalone controller or as an augmentation of a baseline controller.…”

Section: Introductionmentioning

confidence: 65%

L1-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors

Pravitra,

Ackerman,

Cao

et al. 2020

Preprint

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This paper presents a multirotor control architecture, where Model Predictive Path Integral Control (MPPI) and L1 adaptive control are combined to achieve both fast model predictive trajectory planning and robust trajectory tracking. MPPI provides a framework to solve nonlinear MPC with complex cost functions in real-time. However, it often lacks robustness, especially when the simulated dynamics are different from the true dynamics. We show that the L1 adaptive controller robustifies the architecture, allowing the overall system to behave similar to the nominal system simulated with MPPI. The architecture is validated in a simulated multirotor racing environment.

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Section: Introductionmentioning

confidence: 65%

L1-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors

Pravitra,

Ackerman,

Cao

et al. 2020

Preprint

Self Cite

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“…The designer can choose the cutoff frequency for the low pass filter, removing the highfrequency components of the adaptation from the control input. This promotes a very fast adaptation rate while keeping the control input sufficiently smooth, making L 1 a much more practical adaptive control technique [6], [2]. L 1 attitude controllers have been developed for attitude control of quadrotors, using Euler angles (or quaternions) as attitude states [25], [19], [5], [17] or for general linear sys-tems [4].…”

Section: A Related Workmentioning

confidence: 99%

Geometric L1 Adaptive Attitude Control for a Quadrotor Unmanned Aerial Vehicle

Kotaru

Edmonson

Sreenath

2019

Journal of Dynamic Systems, Measurement, and Control

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In this paper, we study the quadrotor UAV attitude control on SO(3) in the presence of unknown disturbances and model uncertainties. L1 adaptive control for UAVs using Euler angles/quaternions is shown to exhibit robustness and precise attitude tracking in the presence of disturbances and uncertainties. However, it is well known that dynamical models and controllers that use Euler angle representations are prone to singularities and typically have smaller regions of attraction while quaternion representations are subject to the unwinding phenomenon. To avoid such complexities, we present a Geometric L1 adaptation control law to estimate the uncertainties. A model reference adaptive control approach is implemented, with the attitude errors between the quadrotor model and the reference model defined on the manifold. Control laws for the quadrotor and reference models are developed directly on SO(3) to track the desired trajectory while rejecting the uncertainties. Control Lyapunov function based analysis is used to show the exponential input-to-state stability of the attitude errors. The proposed L1 adaptive controller is validated using numerical simulations. Preliminary experimental results are shown comparing a geometric PD controller to the geometric L1 adaptive controller. Experimental validation of the proposed controller is carried out on an Autel X-star quadrotor.

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“…Theorem 1 Consider the system in (3) and the controller in (16)-( 22) subject to conditions in (32) and (33). Letγ 0 > 0 be a given arbitrarily small constant.…”

Section: Lemmamentioning

confidence: 99%

“…The low-level controller is a multirate L 1 adaptive controller for tracking the generated reference command by compensating for uncertainties and disturbances. L 1 adaptive controller is a robust control technique with quantifiable performance bounds and robustness margins [29,30,31], which has been successfully implemented on manned and unmanned aircraft [32,33,34] and simulation models [35,36,37,38,39]. In this paper, the L 1 adaptive control theory is extended to the multirate SD framework, while maintaining the key benefits of a continuous-time L 1 adaptive controller, [29,30,40,31].…”

Section: Introductionmentioning

confidence: 99%

A multilevel sampled‐data approach for resilient navigation and control of autonomous systems

Jafarnejadsani

Wan

Hovakimyan

et al. 2019

Intl J Robust & Nonlinear

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SummaryAutonomous systems are rapidly becoming an integrated part of the modern life. Safe and secure navigation and control of these systems present significant challenges in the presence of uncertainties, physical failures, and cyber attacks. In this paper, we formulate a navigation and control problem for autonomous systems using a multilevel control structure, in which the high‐level reference commands are limited by a saturation function, whereas the low‐level controller tracks the reference by compensating for disturbances and uncertainties. For this purpose, we consider a class of nested, uncertain, multiple‐input–multiple‐output systems subject to reference command saturation, possibly with nonminimum phase zeros. A multirate output‐feedback adaptive controller is developed as the low‐level controller. The sampled‐data (SD) design of this controller facilitates the direct implementation on digital computers, where the input/output signals are available at discrete time instances with different sampling rates. In addition, stealthy zero‐dynamics attacks become detectable by considering a multirate SD formulation. Robust stability and performance of the overall closed‐loop system with command saturation and multirate adaptive control are analyzed. Simulation scenarios for navigation and control of a fixed‐wing drone under failures/attacks are provided to validate the theoretical findings.

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L1 Stability Augmentation System for Calspan's Variable-Stability Learjet

Cited by 32 publications

References 5 publications

L1-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors

L1-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors

Geometric L1 Adaptive Attitude Control for a Quadrotor Unmanned Aerial Vehicle

A multilevel sampled‐data approach for resilient navigation and control of autonomous systems

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