International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Multicopters are widely used in a professional manner today in film industry, military applications etc. These vehicles are easy to build which have mechanical and electrical parts. Also autopilots are being sold to control these vehicles. However, mostly these vehicles are being built and used with little knowladge about the system dynamics, performance and stability. Mostly wrong propeller motor battery combinations are used. And mostly these vehicles are being used by different payloads. Every change in payload changes dynamics and stability of the vehicle. Very little of users are eligible at PID tuning and increasing the stability of multirotor vehicles. We aim to provide a test platform and methodoly for system identification and increasing stability of Multirotor Vehicles.

In this paper the author investigated the implementation of the convex optimization method in the area of the estimation of model parameters from experimental data. The investigation focused on the identification of processes within the technical environment such as: the liquid flow process, the mechanical vibrating process and the electric arc discharge. The theoretical support related to the convex optimization algorithm is emphasized in the first section of the paper. The preconditions for the implementation of the algorithm within the system identification context are also presented. In the third and the fourth sections, the main analysis is made. The mathematical models of the processes under investigation and the software implementations are depicted. The results showed that for an input signal, equivalent with the Dirac impulse, the disciplined convex optimization algorithm provide consisted estimate of the process under investigation which is similar to the results from the classical least-squares identification algorithm. These results are the basis for further investigations on the implementation of the convex optimization algorithm for system identification.