TY - GEN
T1 - A neural gust load alleviator for aircraft model using active control
AU - Nie, Rui
AU - Zhang, Weiguo
AU - Li, Guangwen
AU - Liu, Xiaoxiong
PY - 2009
Y1 - 2009
N2 - For an aircraft flying in atmosphere a BP neural network controller based on the active control technology is designed. The design goal is to reject the influence of a rotary gust disturbance to the normal overload of the aircraft. In order to improve the dynamic response of such aircraft, the active control technology which will act on the longitudinal control is proposed. The designed controller produces the necessary variation law to improve the ride quality of the passenger. Because the BP net could approximate any nonlinear mathematical model, a kind of BP neural inverse network is presented. However, since the BP algorithm is easy to fall into local optimal value, the particle swarm optimization (PSO) strategy is adopted to train the parameters of the network. Simulation results show that the gust load alleviation (GLA) system designed by neural network could obtain good robust stability, and the capability of restraining gust turbulence as well as measurement noises can be achieved by using the method introduced in this paper.
AB - For an aircraft flying in atmosphere a BP neural network controller based on the active control technology is designed. The design goal is to reject the influence of a rotary gust disturbance to the normal overload of the aircraft. In order to improve the dynamic response of such aircraft, the active control technology which will act on the longitudinal control is proposed. The designed controller produces the necessary variation law to improve the ride quality of the passenger. Because the BP net could approximate any nonlinear mathematical model, a kind of BP neural inverse network is presented. However, since the BP algorithm is easy to fall into local optimal value, the particle swarm optimization (PSO) strategy is adopted to train the parameters of the network. Simulation results show that the gust load alleviation (GLA) system designed by neural network could obtain good robust stability, and the capability of restraining gust turbulence as well as measurement noises can be achieved by using the method introduced in this paper.
KW - Direct force control
KW - Gust load alleviation
KW - Neural network
KW - Particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=77949620574&partnerID=8YFLogxK
U2 - 10.1109/ICICISYS.2009.5357893
DO - 10.1109/ICICISYS.2009.5357893
M3 - 会议稿件
AN - SCOPUS:77949620574
SN - 9781424447541
T3 - Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
SP - 204
EP - 208
BT - Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
T2 - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Y2 - 20 November 2009 through 22 November 2009
ER -