Abstract
Any study of the dynamics and control of mechanical systems is based on adequate mathematical models that contain the dynamic parameters of the system under study. Their evaluation, in particular for the tower crane boom system, is a particularly relevant scientific and practical problem, the solution of which will provide the basis for further calculations of the optimal modes of movement of the tower crane slewing mechanism. The research aims to determine the dynamic parameters of the installation (moment of inertia of the slewing mechanism J, torque of dry friction forces M0, driving torque coefficient K1) and to plan experimental studies. The experimental method, numerical optimization methods (in particular, the modified Rot-Ring-PSO method), and statistical methods were used to conduct the research. Based on the results of the experiments, the dynamic parameters of the mathematical model of the laboratory installation of the tower crane slewing mechanism with propeller thrust were identified. The criterion that evaluates the identification error of the parameters K1, M0, and J was formed and minimized using the Rot-Ring-PSO algorithm. Plots of the kinematic characteristics of the movement of the boom system in terms of the angle of rotation of the boom and the speed of rotation of the boom were constructed. When processing the experimental data, the dependence of the error values on the supply voltage of the propeller drive was revealed. The error in the boom rotation speed at the drive supply voltage of 90% (compared to the voltage variant of 40%) decreased by almost 15%, and the error in the boom rotation angle at the drive supply voltage of 90% (compared to the supply voltage variant of 40%) decreased by almost 3 times. The regularity has been confirmed that with an increase in the supply voltage, the error value of the system decreases. In the course of processing the experimental studies, the dynamic parameters of the installation were identified: K1=4.80‧10-8 V/(rpm)2, M0=34.519 Nm, J=24.21 kgm2. The obtained results will be used to optimise the plant’s motion modes, and the developed identification algorithm can be used for other similar problems