IDENTIFICATION OF THE DYNAMIC MODEL FOR THE ROLL CHANNEL OF THE UNMANNED AIR VEHICLE UNDER WEAK EXCITING INPUT SIGNAL

Abstract

In this article has been identified linear stationary roll dynamic model for unmanned air vehicle with the weak exciting input signal and sensor measurements noise using the two-step method, maximum likelihood method and the genetic optimization algorithm. Due to the weak frequency content of the input signal, eigenvalues of the information matrix are close to zero, and the use of the output error method often gives the wrong solutions in each cycle of the identification algorithm based on the Monte Carlo method. Ill-conditioned information matrix in the identification of dynamic model occurs due to linear relationships between variables. The two-step identification method finds the ratio of the parameters in the first stage. In the second step, the identification algorithm is conducted for the vector of parameters with reduced size. The two-stage identification method gives the right solution with the permissible errors for each execution of the identification process