ARF60 AUS-UAV modeling, system identification, guidance and control: Validation through hardware in the loop simulation

Al-Radaideh, Amer; Al‐Jarrah, Mohammad; Jhemi, Ali; Dhaouadi, Rached

doi:10.1109/isma.2009.5164799

Cited by 15 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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]. The first step is to design flight experiments to collect the data with sufficient accuracy and duration.…”

Section: System Identification For Motor Coefficientsmentioning

confidence: 99%

“…Small drones notably rely on accurate self-location information for guidance, navigation, and control. Drone self-localization typically counts on IMUs [12][13][14], the Global Positioning System (GPS) [15] (differential GPS [16]), infrared (IR) sensors [17], laser rangefinders [18,19], and optical and vision systems [20][21][22]. While these sensing systems have successfully supported outdoor applications, extensive investment has been made to enhance the capability of self-localization for drone by improving the GPS infrastructure, utilizing cellular network infrastructure [23], or integrating both technologies for a wider range of applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Al-Radaideh¹,

Sun²

2021

Drones

Self Cite

View full text Add to dashboard Cite

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.

show abstract