Feasibility of In-Flight Quadrotor Individual Motor Thrust Measurements

Bazin, John; Fields, Travis; Smith, Andrew

doi:10.2514/6.2016-1760

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…Furthermore, advances in MEMS technology [24][25][26][27][28] and rapid prototyping technology makes quadrotor has a simplest electronics and mechanical structures design. Therefore, quadrotor always being considered in research with wide applications and various tasks such as civil commercial, calamity and meteorological observation, agricultural and military purpose such as surveillances, reconnaissance and communication [29][30][31] also educational purposes [32].…”

Section: Quadrotor Uavmentioning

confidence: 99%

Sensor Fusion Algorithm by Complementary Filter for Attitude Estimation of Quadrotor with Low-Cost IMU

2018

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This paper proposes a sensor fusion algorithm by complementary filter technique for attitude estimation of quadrotor UAV using low-cost MEMS IMU. Angular rate from gyroscope tend to drift over a time while accelerometer data is commonly effected with environmental noise. Therefore, high frequency gyroscope signal and low frequency accelerometer signal is fused using complementary filter algorithm. The complementary filter scaling factor K1=0.98 and K2=0.02 are used to merge both gyro and accelerometer. The results show that the smooth roll, pitch and yaw attitude angle can be obtained from the low cost IMU by using proposed sensor fusion algorithm.

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Section: Quadrotor Uavmentioning

confidence: 99%

Sensor Fusion Algorithm by Complementary Filter for Attitude Estimation of Quadrotor with Low-Cost IMU

2018

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“…For three axes rotation, the same concept is applied as shown in Fig. 5 where roll angle ( ) and pitch angle ( ) can be computed as (8) and (9) respectively. The rotation follows typical right-hand rules law:…”

Section: B Accelerometermentioning

confidence: 99%

Sensor Fusion for Attitude Estimation and PID Control of Quadrotor UAV

Noordin¹,

Basri²,

Mohamed³

2018

IJEETC

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This paper presents sensor fusion algorithm using nonlinear complementary filter (NCF) for attitude estimation and a proportional-integral-derivative (PID) controller for small-scale quadrotor unmanned aerial vehicle (UAV) stabilization. From inertial measurement unit (IMU) data, gyroscope as a main sensor is fused to another two sensors; accelerometer and magnetometer to correct drift error of gyroscope to obtain reliable attitude estimation. In this paper, the performance of NCF is implemented on real-time while applying PID controller for attitude stabilization of quadrotor UAV during hovering. Experimental results show the effectiveness of PID controller for quadrotor UAV stabilization during attitude control  .

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“…There, each actuator is assigned a state, which is used to estimate actuator effectiveness, and may detect if an actuator produces a force significantly different from the force an undamaged actuator produces. The direct measurement of individual thrust forces is presented in [10], where a quadcopter is equipped with a strain gauge for each actuator, directly measuring the force that the propeller produces. Being a more direct approach, and not relying on the system's dynamics for estimating the actuator forces means that such an approach should be more robust to model errors (e.g.…”

Section: Introductionmentioning

confidence: 99%