Aerospace and Electronic Systems Magazine September 2016 - 49

Fu, Zhang, and Yu
ited in Figure 10 are not saturated in the climbing and descending maneuvers.
C

Result for following the reference altitude in landing
phase: As can be seen from Figure 11a, the outer-loop
controller can maintain the altitude tracking performance
even in the sharp climbing and descending maneuvers.
Figures 11b, 11c, 11d reveal that the velocity, the pitch
angle, and the flight path angle are controlled to return to
the intended values after the transition maneuver in altitude. As demonstrated in Figure 12, the elevator deflection and throttle angle achieved by the proposed outerloop controller are within the allowable operating ranges.
Result for collision avoidance scenario: Considering
the measurement noise, the angular position with the
magnitude 0.01 rad of white noise and the velocity with
the magnitude 1.5 m/s of white noise are conducted in
the simulation studies. Following the differential flatness-based re-planning algorithm in the longitudinal
plane, the tracking performance of pitch angle is displayed in Figure 13. The blue line (actual pitch angle)
in Figure 13 is fairly close to the red line (desired pitch
angle). This figure confirms that the UAV follows the
commanded pitch angle quite well.

The responses of altitude and velocity are plotted in Figure 14. When the pitch angle is increased, the UAV is forced to
climb up; conversely the UAV has to descend as shown in Figure
14a. Figure 14b exhibits that the UAV velocity is stable in the
entire flight under the energy-loop controller.
From Figure 15, it is clear that even when the UAV is involved in aggressive maneuvers with a large pitch angle, elevator angle and throttle angle are not saturated. Such a desired
performance is contributed by the differential flatness-based
algorithm with consideration of control input constraints.
For interested readers, the demonstration of SAA strategy
for the landing phase performed in this work can be found in the
Concordia's NAV Lab YouTube Channel [29].

CONCLUSION
In this study, a novel algorithm for UAV avoiding multiple
intruders' threats in the landing phase is proposed. The generated trajectory ensures the maximum safety with the minimum
cost of the UAV. The problem of collision avoidance is formulated as a BBO framework to produce a desired trajectory in
real-time. The re-planned path is smoothened by the differential flatness-based approach under the physical constraints
of UAV. Energy-based controller is then applied such that the
UAV can follow the generated trajectory without tracking errors in the longitudinal plane. Numerical simulation studies
are conducted to exemplify the effectiveness of the proposed
algorithm.
Despite that the effectiveness of the proposed scheme has
been validated in MATLAB/Simulink environment, the algorithm has not yet considered permutations including time-vary-

SEPTEMBER 2016

Figure 12.

Elevator angle and throttle angle responses in following reference
altitude scenario.

Figure 13.

Pitch angle of UAV in collision avoidance scenario.

Figure 14.

Actual altitude and velocity in collision avoidance scenario.

Figure 15.

Actual elevator angle and throttle angle in collision avoidance scenario.

IEEE A&E SYSTEMS MAGAZINE

Table of Contents for the Digital Edition of Aerospace and Electronic Systems Magazine September 2016