An adaptive system identification method for a micro unmanned helicopter robot

Abstract

Focusing on the dynamic model parameter identification problem, this paper proposed an adaptive linear-time-domain system identification method for the micro unmanned helicopter robot. Based on the flash memory in the Micro Guide Navigation Control (MGNC), system recorded the flight data sequences regarding the input signal for servos and output signals for attitude and velocity information. Through the adaptive genetic algorithm, the system can construct the high precise dynamic state space model for the micro unmanned helicopter robot. Finally, the effectiveness of the identified model is verified by a series of simulation and tests. The micro unmanned helicopter robot can finish hover, turn, and straight flight tasks.