Coping with Quadcopter Payload Variation via Adaptive Robust Control

Kourani, Ahmad; Kassem, Kareem; Daher, Naseem

doi:10.1109/imcet.2018.8603047

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…where G = [0, 0, g] is the gravitational acceleration vector with g = 9.81 m s −2 being the gravity constant; R t and R r are the translational and rotational velocity transformation matrices between W and B [19];…”

Section: A Dynamic Modelmentioning

confidence: 99%

“…] is the vector of rotor torques, with K t and K Q being the rotor thrust and torque constants, respectively, L is the arm length, and ω i is the i th rotor angular speed with i ∈ [1,2,3,4]. The control inputs, ω i , are computed from the above equations as done in [19]. The terms ∆ ξ and ∆ η ∈ R 3 are the unmodeled nonlinearities and external disturbances [19], [20].…”

Section: A Dynamic Modelmentioning

confidence: 99%

“…The control inputs, ω i , are computed from the above equations as done in [19]. The terms ∆ ξ and ∆ η ∈ R 3 are the unmodeled nonlinearities and external disturbances [19], [20].…”

Section: A Dynamic Modelmentioning

confidence: 99%

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Leveraging PID Gain Selection Towards Adaptive Backstepping Control for a Class of Second-Order Systems

Kourani,

Daher

2021

Preprint

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In this work, we establish a convenient similarity between an adaptive backstepping control law and a standard proportional-integral-derivative (PID) controller for a class of second-order systems. The extracted similarity provides a deeper understanding of the adaptive backstepping design from a performance perspective via an intuitive method to select its otherwise abstract controller gains, on top of its traditional stability perspective. Such a similarity analysis opens the door for researchers to use well-established PID tuning methods to predict the performance of Lyapunov stability-based controllers. At the same time, the obtained formulation reveals how the corresponding PID control law can be linked to Lyapunov stability theory. The proposed scheme is applied to a quadrotor unmanned aerial vehicle (UAV) carrying a payload with the presence of a wind gust as an external disturbance, in simulation and experimentally.

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Section: A Dynamic Modelmentioning

confidence: 99%

Section: A Dynamic Modelmentioning

confidence: 99%

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Leveraging PID Gain Selection Towards Adaptive Backstepping Control for a Class of Second-Order Systems

Kourani,

Daher

2021

Preprint

Self Cite

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“…Adaptive particle swarm optimization was used in a PD controller (APSO-PD) in [20] to improve the rise time, settling time, overshoot, and peak time of a standard PSO controller. Robust adaptive control based on backstepping is used in [21] [22] to provide robust quadcopter altitude and attitude tracking after payload change. Immersion and invariance based adaptive backstepping control is used in [23] to remedy quadcopter attitude instabilities due to disturbance torques or parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive PID Autotuner for Multicopters with Experimental Results

Spencer¹,

Lee²,

Paredes³

et al. 2021

Preprint

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This paper develops an adaptive PID autotuner for multicopters, and presents simulation and experimental results. The autotuner consists of adaptive digital control laws based on retrospective cost adaptive control implemented in the PX4 flight stack. A learning trajectory is used to optimize the autopilot during a single flight. The autotuned autopilot is then compared with the default PX4 autopilot by flying a test trajectory constructed using the second-order Hilbert curve. In order to investigate the sensitivity of the autotuner to the quadcopter dynamics, the mass of the quadcopter is varied, and the performance of the autotuned and default autopilot is compared. It is observed that the autotuned autopilot outperforms the default autopilot.

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Parameter estimation and control of an unmanned aircraft‐based transportation system for variable‐mass payloads

Alatorre

Espinoza

Sanchez

et al. 2021

Asian Journal of Control

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This paper deals with the development of an adaptive robust controller for an Unmanned Aircraft System with variable mass. The goal is to improve trajectory tracking and energy performance while the aircraft mass is gradually reducing due to payload release. To improve the trajectory tracking performance, we propose an algorithm that merges the concepts of least squares method and sliding mode observer for the estimation of the vehicle mass. The nonlinear observer estimates the linear velocities and accelerations, which are further used to obtain the vehicle mass. A robust adaptive pole placement controller based on the Attractive Ellipsoid Method uses this estimation to update the controllers gains due to the mass variation. To validate the effectiveness of the proposed algorithm for performing aerial transportation and deployment of payloads, we present a numerical example comparing the performance of the proposed method with respect to using Proportional Derivative controllers as well as Robust Controllers. KEYWORDSload transportation, model in the loop, nonlinear observer, robust adaptive control, UAS, variable mass 1 INTRODUCTION Diverse research works have explored original solutions for the Unmanned Aircraft System (UAS)-based payload transportation problem for constant and variable mass payloads. As a few examples, the authors in Kui et al. [1]developed an autonomous vehicle to transport a payload by using a tensor embedded in the vehicle, while the authors in Geng and Langelaan [2] designed strategies for load transportation that involves a team of cooperative vehicles. Previously, the authors in Haus et al. [3] presented the MORUS project where they conducted the

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Coping with Quadcopter Payload Variation via Adaptive Robust Control

Cited by 12 publications

References 15 publications

Leveraging PID Gain Selection Towards Adaptive Backstepping Control for a Class of Second-Order Systems

Leveraging PID Gain Selection Towards Adaptive Backstepping Control for a Class of Second-Order Systems

An Adaptive PID Autotuner for Multicopters with Experimental Results

Parameter estimation and control of an unmanned aircraft‐based transportation system for variable‐mass payloads

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