Building Model Using System Identification For Certain Micro Air Vehicle

As an important content of modern control theory and signal process, system identification is a developed subject in several decades recently. It focuses on how to find a control mathematics model for the object of experiment using the data of experiment. The thesis studies the modeling method of MAV using system identification. Firstly, the paper introduces the background of abroad and domestic about Micro Air Vehicles. The basic knowledge of system identification is also introduced. Then the hardware design for modeling of MAV is discussed, it include the structure of data collect device, effect of errors in installation on calibration of angle velocity gyroscope etc. In follow, the paper discusses the experiment design of MAV. Based on the analysis of the proper aerodynamic characteristics of fixed-wing MAV, we can build the longitudinal model and the lateral model for MAV. It deeply discusses the experiment signal design of input and output, identification and elimination of outliers in measurement data. Two identification methods are used in the paper. They are Unbiased least square estimate method and neural network method. The correctness of the model for MAV will be proved by cross test method of the experiment data. The result indicates that the method of system identification can be used for MAV.