Adaptive Kalman Filter for Active Magnetic Bearings Using softcomputing
Magnetic bearings can not only solve the bearing wear and life problems but also reduce the loss and noise of bearing. However, the strong disturbance and noise from the system affects the control behavior. Based on the Kalman filter the influence of noise will be reduced. But the strong nonlinear and uncertainty of parameter of the magnetic bearings make it difficult to establish the estimation / prediction equation in Kalman filter. This paper presents a design method of system estimation / prediction for Kalman filter with using soft computing. Firstly, linear local model for axial magnetic bearing overall system will be deduced. Then a few system parameters, which is relative with nonlinear and uncertainty, will be obtained by a intelligence function, which uses soft computing algorithm as system identification. Finally, the identified system parameters will be used in state equation in Kalman filter. It aims at better filter performance and state estimation than the conventional linear Kalman filter.
Booktitle: Proceedings of ISMB14