TY - GEN
T1 - System identification based on DNNs with disturbance observer and application to unmanned aerial vehicles
AU - Yi, Yang
AU - Zheng, Weixing
AU - Yang, Yi
AU - Guo, Lei
PY - 2013
Y1 - 2013
N2 - In this paper, dynamic neural networks (DNNs) are used as the on-line identifier for a class of nonlinear systems with unknown external disturbance and unknown multiple dead zone actuators. By integrating the novel nonlinear disturbance observer with adaptive control algorithms, the parameter coupling problem between unknown dead zone and DNNs can be successfully solved and the multiple disturbances can also be rejected simultaneously. Both the observation error and the identification error can be proved to convergent to zero. Furthermore, by combining with the numerical result of an unmanned aerial vehicle (UAV) model, the effectiveness of theoretical algorithms can be fully verified.
AB - In this paper, dynamic neural networks (DNNs) are used as the on-line identifier for a class of nonlinear systems with unknown external disturbance and unknown multiple dead zone actuators. By integrating the novel nonlinear disturbance observer with adaptive control algorithms, the parameter coupling problem between unknown dead zone and DNNs can be successfully solved and the multiple disturbances can also be rejected simultaneously. Both the observation error and the identification error can be proved to convergent to zero. Furthermore, by combining with the numerical result of an unmanned aerial vehicle (UAV) model, the effectiveness of theoretical algorithms can be fully verified.
UR - http://handle.uws.edu.au:8081/1959.7/537541
UR - http://ccc.amss.ac.cn/2013/en/
M3 - Conference Paper
SN - 9789881563835
SP - 1787
EP - 1791
BT - Proceedings of the 32nd Chinese Control Conference (CCC 2013), July 26-28, 2013, Xi'an, China
PB - I.E.E.E.
T2 - Chinese Control Conference
Y2 - 26 July 2013
ER -