Flight Experimental Results
Flight tests are carried out to demonstrate the effectiveness of the proposed MOBADC system. The flying arena and system configuration are presented first. Then, four groups of flight tests are carried out as can be observed from in Fig. 4.4. The corresponding performance is evaluated in terms of average absolute position error and standard deviation in comparison with the desired trajectory. The ground truth is provided by OptiTrack system®[1] (a motion capture system with milimeter-level positioning accuracy).
FIGURE 4.4: Four groups of flight tests with different motion scenarios and disturbance types.
Flying Arena and System Configuration
The flying arena (Fig. 4.1) consists of a global sensing (the motion capture system), a base station module with local server, a wireless command interface, off-board estimation and control modules, and on-board sensing, actuation, estimation and control (conducted on QDrone® platform[2]). The connections between each component are presented in Fig. 4.5.
The Intel® Aero Compute Board is utilized as the on-board MCU, which is a purpose-built, UAV developer kit powered by a quad-core Intel® Atom™ processor. The size of a standard playing card features abundant storage capabilities, 802.1 lac Wi-Fi, industry standard interfaces, and reconfigurable I/O to facilitate connecting to a broad variety of quadrotor UAV hardware
FIGURE 4.5: Diagram of major system components.
subsystems. Embedded BM160 IMU, BMM150 magnetometer, and MS5611 barometer sensors support on-board sensing and estimation.
The MCU on quadcopter UAV actively sends on-board sensing data to the base station for high-level mission planning and receives mission commands through a Wi-Fi transceiver module. All of the data collected and estimates obtained by quadrotor will be calibrated and filtered first. Then, the corrected information together with the received mission commands goes through the trajectory generation module. Next, the desired trajectory will be sent to the position control. The outputs will be transformed into attitude command and further into pulse-width modulating (PWM) signals to drive the quadrotor UAV. The above setup and configuration serve for the following flight experiments followed by performances analysis.
Quadcopter Flight Scenarios
The proposed strategy is implemented on the quadrotor UAV with the same configuration as described previously. Note that the control gains are not changed in the case of disturbances imposed. The adjustment of control law lies in the outputs of DO and ESO. This is also the advantage of the MOBADC. Four groups of real-world flight tests (Fig. 4.4) are conducted in a laboratory environment as shown in Fig. 4.1. These experiments also demonstrate the advantages of the proposed method against both cable- suspended-payload disturbance and wind disturbance. The experimental parameters can be found in Appendix. The flight test videos can be found at https://youtu.be/6Fql-aA-ZsM.
Test 1
To demonstrate the capability of the presented MOBADC against the cable-suspended-payload disturbance, a hovering flight under this disturbance is conducted first and the test can be checked in Fig. 4.4. In the course of the quadrotor UAV flying, the payload is static first and then forced to oscillate artificially. During the payload swings, the proposed DO based controller is triggered and the payload’s oscillation amplitude remains the same during the whole process. Without loss of generality, the payload is forced to swing along у direction, a, is approximately equal to A- g/L s~l where L is the length of the cable. Three main phases are included in this test, namely, Phase I: static payload using classical PID method only, Phase II: oscillating payload using classical PID method only, and Phase III: oscillating payload using the proposed controller with DO. The evolutions of the estimated disturbance of DO and the quadrotor’s motion in у direction are shown in Figs. 4.6(a) and 4.6(b), respectively.
As seen from Fig. 4.6(a), compared with the static phase, the proposed DO is able to perceive and estimate the payload oscillating disturbance. This disturbance estimate is fed into our proposed position controller and then
FIGURE 4.6: Hovering flight under payload oscillating disturbance of Test 1: (a) and (h) depict the estimated disturbance and flight trajectory without/with DO in у direction, respectively. The dash and solid curves represent the desired and actual path.
the UAV’s oscillation amplitude is reduced to around 1 cm as shown in Fig. 4.6(h). Obviously, the flight performance demonstrates that the proposed DO based controller is able to estimate the cable-suspended-payload disturbance efficiently and mitigate the UAVs hovering deviation.
