This document describes a hybrid adaptive control approach for aerial manipulation using unmanned aerial vehicles. It presents mathematical models for the manipulator arm and quadrotor dynamics. It then describes three control methods - gain scheduling adaptive control, Lyapunov-based model reference adaptive control, and a hybrid approach that switches between these based on a hybrid automaton. Simulation results demonstrate the effectiveness of the hybrid controller in adapting the quadrotor and manipulator gains for stable pose and torque control during aerial manipulation tasks.

1. Hybrid Adaptive Control for
Aerial Manipulation
By: Pourya Parsa
For
Professor: A.H Mazinan
dec 2017
Islamic Azad University Central
Tehran Branch
Parsa.poorya@yahoo.com

2. • Journal of Intelligent & Robotic Systems
• with a special section on Unmanned Systems
• Editor-in-Chief: Kimon P. Valavanis
• ISSN: 0921-0296 (print version)
ISSN: 1573-0409 (electronic version)
• Journal no. 10846

3. Abstract

4. MRACPI-D

5. 1-Introduction
UAVmanipulate objects
MRACPI-D
1.1 Pose/Wrench
Control
1.2 Slung Load
1.3 Single DOF
Grippers

6. 1.1 Pose/Wrench
Control
1.2 Slung Load
• It could provide unlimited supply of electricity
• It could also be used as additional pose and position
measurement system

7. MM-Ua
1.3 Single DOF
Grippers
Fig. 1 MM-UAV carrying
a 40 cm long cylinder
rod

8. 2-Mathematical Model
3
1
2RRRR
Fig. 3 Complete model
of the mobile manipulating unmanned
aerial system

9. Fig. 2 Reference frames for manipulator arms. See Fig. 3 for detail on overall
UAV-manipulator system

10. 2.1 Manipulator Model
D-H223
θ, d, a,αD-Hi=[A,B[

11. D-HLT‫مممم‬ ‫مممم‬ ‫مم‬L4.‫ممم‬ ‫ممم‬ ‫ممممم‬
2

13. 2.2 Quadrotor Model
T
QTZ

14. )aircraft thrust(
T

15. 2.3 Mobile Manipulating Unmanned Aerial
Vehicle Dynamics

16. 2.4 Center of Mass and Moment
of Inertia Distribution
CM

17. 3 Angle Control Stability
MM-UAVPI-D4PI-D
Fig. 4 Attitude model
reference adaptive PI-D
control

18. J = Ixx, Iyy‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬PID‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬4.‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬‫ه‬
4

19. Kp Ki KdPIDKm Tm
12

20. 5
Fig. 5 Visualization of the stability criterion

21. 4 Hybrid Adaptive Control
11
*Km
*Tm
*β ‫یارش‬‫ط‬‫یارش طططط‬
*J2.4

22. 4.1 Gain Scheduling Adaptive Controller
KDqAqB

23. DOF
gain schedulingKD
KD
J(qA, qB(

24. gain schedulingMRACgain scheduling
adapting the Kp and Ki gains
with respect to joint angle changes, qA and qB respectively,
while maintaining the stability condition
℘ > J(qA, qB).
18Gain scheduling *

25. Fig. 6 Visualization of the gain scheduling
algorithmbased
on maintaining the stability condition > J(qA, qB)℘

26. 4.2 Lyapunov Based MRAC Control
*KD
4.2.1 Stability Analysis
KmJTmBIPT1

27. PI-Dζ ‫یارش‬
e
22

28. 22γyk
7PI-D
JKmTm

30. 510t=5 (Tm=5
γ17a 17b 18MRAC
MRAC

31. u1u3pitchMRAC
torqwe gravity

32. 4.2.2 Simulation verif ication

34. 4.3 Hybrid Automaton
MRACgain sceduling11

35. 5 Conclusions
MRACGain SchedulingLyapunovMRACMRACPI-D
MRAC

36. Parsa.poorya@yahoo.com