Parameter identification for a quadrotor helicopter using PSO

For some real systems, physical parameters are generally unknown and cannot be measured precisely. Moreover, a multiple degrees of freedom unmanned aerial vehicle (UAV) usually has many physical parameters, and the method of obtaining them is challenging. Much research has been done in this area and a lot of methods have been applied to several UAVs. In this paper, we use particle swarm optimization (PSO) swarm intelligence algorithm to identify the inertia physical parameters of quadrotor helicopter. To primarily reduce complexity of the problem and simplify the design of parameter identification scheme, the dynamics of the whole system is decomposed into two subsystems by model transformation. Then using input and output data which come from model test, the model parameters of quadrotor helicopter are identified successfully. The simulating results validate that this scheme has not only good performance on convergence speed, but also low identification error.