Real-Time Closed-Loop System Identification of a Quadcopter

Alabsi, Mohammed; Fields, Travis

doi:10.2514/1.c034219

Cited by 22 publications

(10 citation statements)

References 14 publications

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“…In the context of control of multirotor vehicles, the typical modelling approach has been to use a hover or 'static' model, that is, the thrust and rotor torque are proportional to the square of the propeller rotation rate, and to neglect H-force, pitching and rolling moments [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. As shown in [15], this model breaks down when a quadrotor flies at moderate speeds.…”

Section: Literature Reviewmentioning

confidence: 99%

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Gill

D’Andrea

2019

Drones

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Two low-order, parametric models are developed for the forces and moments that a rotating propeller undergoes in forward flight. The models are derived using a first-principles-based approach, and are computationally efficient in the sense of being represented by explicit expressions. The parameters for the models can be identified either using supervised learning/grey-box fitting from labelled data, or can be predicted using only the static load coefficients (i.e., the hover thrust and torque coefficients). The second model is a multinomial model that is derived by means of a Taylor series expansion of the first model, and can be viewed as a lower-order lumped parameter model. The models and parameter generation methods are experimentally tested against 19 propellers tested in a wind tunnel under oblique flow conditions, for which the data is made available. The models are tested against 181 additional propellers from existing datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Gill

D’Andrea

2019

Drones

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“…For this work, multisine inputs are applied directly to control amplitudes as shown in Alabsi and Fields . 19…”

Section: Methodsmentioning

confidence: 99%

“…For this work, multisine inputs are applied directly to control amplitudes as shown in Alabsi and Fields . 19 Excitation input u n applied to the nth individual control command, is composed of a set of summed harmonic sinusoids with individual phase shifts k , as given in equation (6).…”

Section: Multisine Input Designmentioning

confidence: 99%

“…Optimize multisine input power 1: % initialize excitation frequencies bins and phase shifts 2: % specify transfer function parameters 3: Table 1) in which only two signals were activated during pitch or roll single-axis testing, or all four signals were active during yaw single-axis testing. White Gaussian noise was added to the generated simulation output with a signal-to-noise ratio (SNR) greater than 20 (Alabsi and Fields 19 presents a detailed study about the influence of SNR on parameter estimation accuracy). In the roll and yaw constrained motion open loop testing, the quadcopter frame oscillates in quasi-steady state for 40 seconds duration considering the initial multisine input design 0 .…”

Section: Multisine Input Power Optimization Criteriamentioning

confidence: 99%

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Quadrotor aircraft intelligent system identification experiment design

Alabsi

Fields

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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Aircraft prototyping and modeling is usually associated with resource expensive techniques and significant post flight analysis. The NASA Learn-To-Fly concept targets the replacement of the conventional ground-based aircraft model development and prototyping approaches with an efficient real time paradigm. The work presented herein describes the development of an intelligent excitation input design technique that determines excitation frequencies based on predefined rotational motion dynamic model. The input design is then evaluated on quadcopter unmanned aircraft that utilizes the new multisine input design. In order to minimize flight excursions without compromising the modeling capabilities, multisine input power spectrum is optimized based on the vehicle’s frequency response. The proposed methodology emphasizes excitation of modal frequencies which yields flight data rich information content. The generated optimized multisine input design is utilized for a quadcopter aircraft system identification and the performance is compared to conventional uniform amplitudes design. Simulation results show highly accurate model estimation in all identification results in addition to reduction of induced perturbations and power consumption. Additionally, the generated model prediction capabilities are not compromised after power spectrum optimization. Overall, the proposed technique introduces an efficient and intelligent system identification experiment design that can minimize the time and effort spent during excitation input design.

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“…Similar to the previous multirotor research, 25–29 in this study, a quadrotor unmanned aircraft was utilized as a sample UAV. Seven pilots participated and individually operated the quadcopter system to accomplish the task of following a desired path.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of pilot and quadcopter performance from open-loop mission-oriented flight testing

Zahed

Fields

2021

Proceedings of the Institution of Mechanical Engineers, Part G:

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Unmanned aircraft systems (UAS) have experienced tremendous growth through both commercial (i.e., toys and videography) and defense avenues. The rapid expansion, particularly in the consumer market, has outpaced regulatory bodies. Certification to commercially operate such vehicles currently requires the successful completion of a knowledge examination, without the need to physically operate a vehicle. The focus of the work presented herein is on quantifying the pilot and multi-rotor performance in an attempt to provide quantitative metrics that can be used to establish training and certification for pilots and aircraft. Test pilots were categorized based on their experience level, and the quadrotor unmanned aircraft was categorized based on the flight control mode. Cross-track command (CTC) and path error (PE) were calculated as potential time-domain metrics to quantify pilot and quadcopter performance. Individual binary logistic regression models were developed to identify the pilot experience level (PEL) and UAV control level (UCL) from the decision tree outcomes. A verification test case was included to evaluate the established regression models. Results show that the models can evaluate pilot and quadcopter performance individually, which can be used to develop the pilot training curriculum and/or evaluate pilot effectiveness in specific flight scenarios.

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Real-Time Closed-Loop System Identification of a Quadcopter

Cited by 22 publications

References 14 publications

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Quadrotor aircraft intelligent system identification experiment design

Evaluation of pilot and quadcopter performance from open-loop mission-oriented flight testing

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