L1 Adaptive Controller for Attitude Control of Multirotors

Mallikarjunan, Srinath; Nesbitt, Bill; Kharisov, Evgeny; Xargay, Enric; Hovakimyan, Naira; Cao, Chengyu

doi:10.2514/6.2012-4831

Cited by 45 publications

(30 citation statements)

References 7 publications

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“…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]. Geometric adaptive schemes have been previously developed for quadrotor control in [14], [12].…”

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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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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“…This allows arbitrarily high adaptation gains to be chosen for fast adaptation and to determine uniform bounds for the system's state and control signals [5]. Attitude control based on L 1 adaptive control was shown in [6], where three algorithms were successfully implemented and tested on a quadrotor, hexacopter and octocopter, respectively. L 1 adaptive output feedback on translational velocity was successfully implemented on a quadrotor to compensate for artificial reduction in the speed of a single motor [7].…”

Section: Introductionmentioning

confidence: 99%

Transfer learning for high‐precision trajectory tracking through adaptive feedback and iterative learning

Pereida

Kooijman

Duivenvoorden

et al. 2018

Adaptive Control & Signal

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Robust and adaptive control strategies are needed when robots or automated systems are introduced to unknown and dynamic environments where they are required to cope with disturbances, unmodeled dynamics, and parametric uncertainties. In this paper, we demonstrate the capabilities of a combined  1 adaptive control and iterative learning control (ILC) framework to achieve high-precision trajectory tracking in the presence of unknown and changing disturbances. The  1 adaptive controller makes the system behave close to a reference model; however, it does not guarantee that perfect trajectory tracking is achieved, while ILC improves trajectory tracking performance based on previous iterations. The combined framework in this paper uses  1 adaptive control as an underlying controller that achieves a robust and repeatable behavior, while the ILC acts as a high-level adaptation scheme that mainly compensates for systematic tracking errors. We illustrate that this framework enables transfer learning between dynamically different systems, where learned experience of one system can be shown to be beneficial for another different system. Experimental results with two different quadrotors show the superior performance of the combined  1 -ILC framework compared with approaches using ILC with an underlying proportional-derivative controller or proportional-integral-derivative controller.Results highlight that our  1 -ILC framework can achieve high-precision trajectory tracking when unknown and changing disturbances are present and can achieve transfer of learned experience between dynamically different systems. Moreover, our approach is able to achieve precise trajectory tracking in the first attempt when the initial input is generated based on the reference model of the adaptive controller. KEYWORDSiterative learning,  1 adaptive control, trajectory tracking, transfer learning INTRODUCTIONRobots and automated systems are being deployed in unstructured and continuously changing environments. Sophisticated control methods are required to guarantee high overall performance in these environments where model uncertainties, unknown disturbances, and changing dynamics are present. Examples of robotic applications in unknown 388

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“…This algorithm has been successfully implemented on UAVs to augment a baseline controller for improved disturbance rejection. Attitude control based on L 1 adaptive control was shown in [8], where three algorithms were successfully implemented and tested on a quadrotor, hexacopter and octocopter, respectively. In [9], L 1 adaptive control is implemented for a quadrotor in translational velocity output feedback control, and shows the ability of the controller to compensate for artificial reduction in the speed of a single motor.…”

Section: Introductionmentioning

confidence: 99%

High-precision trajectory tracking in changing environments through L<inf>1</inf> adaptive feedback and iterative learning

Pereida

Duivenvoorden

Schoellig

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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As robots and other automated systems are introduced to unknown and dynamic environments, robust and adaptive control strategies are required to cope with disturbances, unmodeled dynamics and parametric uncertainties. In this paper, we propose and provide theoretical proofs of a combined L1 adaptive feedback and iterative learning control (ILC) framework to improve trajectory tracking of a system subject to unknown and changing disturbances. The L1 adaptive controller forces the system to behave in a repeatable, predefined way, even in the presence of unknown and changing disturbances; however, this does not imply that perfect trajectory tracking is achieved. ILC improves the tracking performance based on experience from previous executions. The performance of ILC is limited by the robustness and repeatability of the underlying system, which, in this approach, is handled by the L1 adaptive controller. In particular, we are able to generalize learned trajectories across different system configurations because the L1 adaptive controller handles the underlying changes in the system. We demonstrate the improved trajectory tracking performance and generalization capabilities of the combined method compared to pure ILC in experiments with a quadrotor subject to unknown, dynamic disturbances. This is the first work to show L1 adaptive control combined with ILC in experiment.

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L1 Adaptive Controller for Attitude Control of Multirotors

Cited by 45 publications

References 7 publications

Geometric L1 Adaptive Attitude Control for a Quadrotor Unmanned Aerial Vehicle

Geometric L1 Adaptive Attitude Control for a Quadrotor Unmanned Aerial Vehicle

Transfer learning for high‐precision trajectory tracking through adaptive feedback and iterative learning

High-precision trajectory tracking in changing environments through L<inf>1</inf> adaptive feedback and iterative learning

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