TY - JOUR
T1 - PD+SMC Quadrotor Control for Altitude and Crack Recognition Using Deep Learning
AU - Vazquez-Nicolas, J. M.
AU - Zamora, Erik
AU - González-Hernández, Iván
AU - Lozano, Rogelio
AU - Sossa, Humberto
N1 - Publisher Copyright:
© 2020, ICROS, KIEE and Springer.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - Building inspection is a vital task because infrastructure damage puts people at risk or causes economic losses. Thanks to the technological breakthroughs in regard to Unmanned Aerial Vehicles (UAVs) and intelligent systems, there is a real possibility to implement an inspection by means of these technologies. UAVs allow reaching difficult places and, depending on the hardware carried onboard, take data or compute algorithms to understand the environment. This paper proposes a real-time robust altitude control strategy for a quadrotor aircraft, also a convolutional neuronal network for crack recognition is developed. The main idea of this proposal is to lay the background for an autonomous system for the inspection of structures using a UAV. For the robust control, a combination of two control actions, one linear (PD) and another nonlinear (Sliding Mode) is used. The combination of these control actions allows increasing the system’s performance. To verify the satisfactory performance of proposed control law, simulations and experimental results with a quadrotor, in the presence of disturbances, are presented. For crack recognition in images, several experiments were carried out validating the proposed model. For CNN training, a database of cracks was built from images taken from the Internet.
AB - Building inspection is a vital task because infrastructure damage puts people at risk or causes economic losses. Thanks to the technological breakthroughs in regard to Unmanned Aerial Vehicles (UAVs) and intelligent systems, there is a real possibility to implement an inspection by means of these technologies. UAVs allow reaching difficult places and, depending on the hardware carried onboard, take data or compute algorithms to understand the environment. This paper proposes a real-time robust altitude control strategy for a quadrotor aircraft, also a convolutional neuronal network for crack recognition is developed. The main idea of this proposal is to lay the background for an autonomous system for the inspection of structures using a UAV. For the robust control, a combination of two control actions, one linear (PD) and another nonlinear (Sliding Mode) is used. The combination of these control actions allows increasing the system’s performance. To verify the satisfactory performance of proposed control law, simulations and experimental results with a quadrotor, in the presence of disturbances, are presented. For crack recognition in images, several experiments were carried out validating the proposed model. For CNN training, a database of cracks was built from images taken from the Internet.
KW - Deep learning
KW - UAV
KW - embedded control system
KW - inspection
KW - quadrotor aircraft
KW - robust altitude control
UR - http://www.scopus.com/inward/record.url?scp=85074845112&partnerID=8YFLogxK
U2 - 10.1007/s12555-018-0852-9
DO - 10.1007/s12555-018-0852-9
M3 - Artículo
AN - SCOPUS:85074845112
SN - 1598-6446
VL - 18
SP - 834
EP - 844
JO - International Journal of Control, Automation and Systems
JF - International Journal of Control, Automation and Systems
IS - 4
ER -