Annealing robust nonlinear adaptive inverse control with FNNBSVR for magnetic bearing systems

In this paper, a new design procedure of nonlinear adaptive inverse control with the fuzzy neural networks based on support vector regression (FNNBSVR) and annealing robust learning algorithm (ARLA) is proposed for the magnetic bearing systems. The FNNBSVR is used to overcome initial structure problem and long training time in the nonlinear adaptive inverse control. Besides, the ARLA is proposed to overcome the outlier in the training procedure. It turns out that the proposed method can use less training time to get the FNNBSVR plant, inverse FNNBSVR plant and fuzzy neural network (FNN) controller to overcome the outlier in noise. Finally, this proposed method is applied to control magnetic bearing system. The experimental results show that the proposed method provides a greater flexibility and better performance in controlling magnetic bearing systems.