Quadrotor Model Generation using System Identification Techniques

Angarita, John E.; Schroeder, Kevin; Black, Jonathan

doi:10.2514/6.2018-1917

Cited by 1 publication

(2 citation statements)

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“…In order to compute accurate motor thrust forces using the PWM signals, we present a system-identification strategy in this section to obtain function f * in Equation ( 19) [39]. The system identification process has to go through a few steps to generate f * that maps the input PWM signals to the total motor thrust [13,14,[40][41][42].…”

Section: System Identification For Motor Coefficientsmentioning

confidence: 99%

“…where u(t) and y(t) are the input and output of the system, respectively, e(t) is the system disturbance, G(ξ, η) and H(ξ, η) are the transfer functions of the deterministic and the stochastic parts of the system, respectively, ξ is the backward shift operator, and η is the parameter vector [39]. A subset of the general linear model structure, can be represented as…”

Section: Model Structure Selection Estimation and Validationmentioning

confidence: 99%

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Self-Localization of Tethered Drones without a Cable Force Sensor in GPS-Denied Environments

Al-Radaideh¹,

Sun²

2021

Drones

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This paper considers the self-localization of a tethered drone without using a cable-tension force sensor in GPS-denied environments. The original problem is converted to a state-estimation problem, where the cable-tension force and the three-dimensional position of the drone with respect to a ground platform are estimated using an extended Kalman filter (EKF). The proposed approach uses the data reported by the onboard electric motors (i.e., the pulse width modulation (PWM) signals), accelerometers, gyroscopes, and altimeter, embedded in the commercial-of-the-shelf (COTS) inertial measurement units (IMU). A system-identification experiment was conducted to determine the model that computes the drone thrust force using the PWM signals. The proposed approach was compared with an existing work that assumes known cable-tension force. Simulation results show that the proposed approach produces estimates with less than 0.3-m errors when the actual cable-tension force is greater than 1 N.

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