TY - GEN
T1 - The flight control system based on multivariable PID neural network for small-scale unmanned helicopter
AU - Song, Ping
AU - Qi, Guangping
AU - Li, Kejie
PY - 2009
Y1 - 2009
N2 - How to design the flight control system (FCS) of small-scale unmanned helicopter is still a difficult challenge today. In this paper, one novel control approach based on Multivariable PID Neural Network (MPIDNN) was firstly used to design the FCS of small-scale unmanned helicopter. MPIDNN is suitable for controlling the multi-input multioutput (MIMO), nonlinear, highly coupled, uncertain and dynamic system such as helicopter. The training algorithm based on target function and MPIDNN forwards algorithm was designed in this control system. The sensor module, embedded control board and communication module was designed to provide an operational hardware platform for the control system. The result of simulation indicates that the training algorithm can solve the offline training and study problem of small-scale unmanned helicopter. The forwards algorithm can control the flight of helicopter well and its maximum magnitude of error is about 1%. Simulation shows that the performance of our control approach is perfect.
AB - How to design the flight control system (FCS) of small-scale unmanned helicopter is still a difficult challenge today. In this paper, one novel control approach based on Multivariable PID Neural Network (MPIDNN) was firstly used to design the FCS of small-scale unmanned helicopter. MPIDNN is suitable for controlling the multi-input multioutput (MIMO), nonlinear, highly coupled, uncertain and dynamic system such as helicopter. The training algorithm based on target function and MPIDNN forwards algorithm was designed in this control system. The sensor module, embedded control board and communication module was designed to provide an operational hardware platform for the control system. The result of simulation indicates that the training algorithm can solve the offline training and study problem of small-scale unmanned helicopter. The forwards algorithm can control the flight of helicopter well and its maximum magnitude of error is about 1%. Simulation shows that the performance of our control approach is perfect.
KW - Flight control system
KW - Multivariable PID neural network
KW - Small-scale unmanned helicopter
UR - http://www.scopus.com/inward/record.url?scp=71049170477&partnerID=8YFLogxK
U2 - 10.1109/ITCS.2009.117
DO - 10.1109/ITCS.2009.117
M3 - Conference contribution
AN - SCOPUS:71049170477
SN - 9780769536880
T3 - Proceedings - 2009 International Conference on Information Technology and Computer Science, ITCS 2009
SP - 538
EP - 541
BT - Proceedings - 2009 International Conference on Information Technology and Computer Science, ITCS 2009
T2 - 2009 International Conference on Information Technology and Computer Science, ITCS 2009
Y2 - 25 July 2009 through 26 July 2009
ER -