In response to the open-loop instability of magnetic bearing systems, this research develops a closed-loop identification method to obtain an accurate plant model for controller design. The identification method is based on a decentralized and decoupling control architecture, which allows the decoupling of the multi-input-multi-output (MIMO) system into two single-input-single-output (SISO) systems for simplifying the identification process. The identification uses a pseudo-random binary sequence as the excitation signal. A parameterized model is obtained through a parameter estimation algorithm based on the output error model. To ensure that the model prediction error can converge during the computation process, a filter is incorporated into the estimation algorithm. The filter can be synthesized systematically by solving a set of linear matrix inequalities. The performance of the proposed closed-loop identification method is firstly verified by simulations. Then it is implemented on a five-axis magnetic bearing platform and the parameterized model obtained from the identification is compared to the experimental frequency response.
