Identification and modeling of the three rotational movements of a miniature coaxial helicopter

Abstract

This paper presents the identification of a miniature coaxial helicopter system. First, the helicopter flying principles are described and the hardware setup of the developed platform is presented. Further, linear models are developed for the movements of the helicopter using prediction error identification methods. The results in this case are accurate and can be used for performant controller design in some operating points. But in order to model the complete dynamics of the helicopter, nonlinear models are developed using recurrent dynamic neural networks. In this case the models obtained present a higher accuracy compared with the linear case and also with the results published until now. In the end, the advantages of nonlinear modeling based on neural networks is emphasized and some conclusions are drawn.

A parameter adaptive identification method for a pumped storage hydro unit regulation system model using an improved gravitational search algorithm

“…Parameter identification not only provides accurate models for systems in advanced control system design, but also has been used in status inspection, fault diagnosis, and performance prediction. [5][6][7][8] Various parameter identification techniques, such as the least square (LS) method and the piecewise linear polynomial function (PLPF) method are widely studied for the water turbine regulation system (WTRS). Zhang et al 9 proposed an online parameter identification method for the approximate linear model of the hydro turbine on the basis of recursive least squares.…”