Integrated identification modeling of rotorcraft-based unmanned aerial vehicle

Abstract

Developing an autonomous rotorcraft-based unmanned aerial vehicle presents higher level and difficult challenges than most of the robots in general. A miniature rotorcraft, with four control inputs and six degrees of freedom, has an inherently multivariable behavior that exhibits coupling effects among the different axes of motion. The dynamics of this type of aerial vehicle is characterized by instability, high-order and sensitivity to disturbance. For rotorcraft to function as a stable mobile platform in changing flight conditions, therefore, its dynamics must be understood and modeled as the basis for controlling such a vehicle. The paper presents a development of linear model of a small scale helicopter using multi input multi output time domain identification system. The results from first principle approach are used as initial condition in the Prediction Error Minimization scheme to achieve convergence. It is demonstrated that the proposed technique can enhance the accuracy of dynamics model obtained from the first principle prediction. Using the technique, the establishment of global helicopter linear model can be achieved for a practical design of linear control laws.