TY - JOUR
T1 - Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks
AU - Lopez-Sanchez, Ivan
AU - Rossomando, Francisco
AU - Pérez-Alcocer, Ricardo
AU - Soria, Carlos
AU - Carelli, Ricardo
AU - Moreno-Valenzuela, Javier
N1 - Publisher Copyright:
© 2021 The Authors
PY - 2021/10/14
Y1 - 2021/10/14
N2 - In this document, the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor are presented. The introduced scheme relies on a generalized regression neural network (GRNN). The proposed scheme has a structure consisting of an inner control loop inaccessible to the user (i.e., an embedded controller) and an outer control loop which generates commands for the inner control loop. The adaptive GRNN is applied in the outer control loop. The proposed approach lies in the aptitude of the GRNN to estimate the disturbances and unmodeled dynamic effects without requiring accurate knowledge of the quadrotor parameters. The adaptation laws are deduced from a rigorous convergence analysis ensuring asymptotic trajectory tracking. The proposed control scheme is implemented on the QBall 2 quadrotor. Comparisons with respect to a PD-based control, an adaptive model regressor-based scheme, and an adaptive neural-network controller are carried out. The experimental results validate the functionality of the novel control scheme and show a performance improvement since smaller tracking error values are produced.
AB - In this document, the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor are presented. The introduced scheme relies on a generalized regression neural network (GRNN). The proposed scheme has a structure consisting of an inner control loop inaccessible to the user (i.e., an embedded controller) and an outer control loop which generates commands for the inner control loop. The adaptive GRNN is applied in the outer control loop. The proposed approach lies in the aptitude of the GRNN to estimate the disturbances and unmodeled dynamic effects without requiring accurate knowledge of the quadrotor parameters. The adaptation laws are deduced from a rigorous convergence analysis ensuring asymptotic trajectory tracking. The proposed control scheme is implemented on the QBall 2 quadrotor. Comparisons with respect to a PD-based control, an adaptive model regressor-based scheme, and an adaptive neural-network controller are carried out. The experimental results validate the functionality of the novel control scheme and show a performance improvement since smaller tracking error values are produced.
KW - Adaptive control
KW - Generalized regression neural network
KW - Quadrotor
KW - Real-time experiments
UR - http://www.scopus.com/inward/record.url?scp=85111838272&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2021.06.079
DO - 10.1016/j.neucom.2021.06.079
M3 - Artículo
AN - SCOPUS:85111838272
SN - 0925-2312
VL - 460
SP - 243
EP - 255
JO - Neurocomputing
JF - Neurocomputing
ER -