Anotace:
The main goal of this paper is to present the identification methodology of the nonlinear dynamical systems on the Upgraded magnetic levitation model. First, the upgrade of the CE 152 magnetic levitation model and its integration into the Distributed Control Systems (DCS) architecture is presented. The proposed upgrade is focused on the replacement of the outdated MF624 laboratory card with the Board51, which enables integration of the model into the DCS architecture and moves the control loop closer to the system. System identification is performed to leverage the full potential of the upgraded model. This includes the derivation of the mathematical model of the system using analytical identification method and the experimental identification of unknown parameters. Parameters are estimated in the structure of output prediction error using the nonlinear least squares method. The resulting gray-box model is validated in both an open-loop and a closed-loop setup using optimal state control algorithm (LQI) on the real Magnetic levitation model with expected results.