Title :
Fault Detection Design for RUAV with an Adaptive Threshold Neural-Network Scheme
Author :
Qi, Juntong ; Jiang, Zhe ; Zhao, Xingang ; Han, Jianda
fDate :
May 30 2007-June 1 2007
Abstract :
This paper describes recent research on system design of a small-scaled rotorcraft UAV (RUAV) system and a control scheme for model-scaled helicopter. The small-scaled UAV helicopter is to be designed as a test bed for the implementation of new nonlinear control theories. The full system has been tested successfully in the manual operation and obtained useful data, which is to be analyzed and used in identifying the RUAV model and design advanced control technologies. A control methodology for helicopter autopilot, which is based on nonlinear dynamic equations with a simplified thrust-torque generation model valid for hovering and non-aggressive flights, is presented.
Keywords :
helicopters; neural nets; remotely operated vehicles; UAV; adaptive threshold neural-network scheme; fault detection; helicopter; nonlinear dynamic equations; rotorcraft; thrust-torque generation model; Aircraft; Automatic control; Design automation; Fault detection; Fault diagnosis; Helicopters; Laboratories; Robotics and automation; Unmanned aerial vehicles; Vehicle detection; adaptive threshold; fault detection; neural-network; rotorcraft UAV;
Conference_Titel :
Control and Automation, 2007. ICCA 2007. IEEE International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4244-0818-4
Electronic_ISBN :
978-1-4244-0818-4
DOI :
10.1109/ICCA.2007.4376417
