Integration Intelligent Estimators to Disturbance Observer to Enhance Robustness of Active Magnetic Bearing Controller

摘要

One of the essential components of rotary systems is bearing which is supporting load of rotor. Active magnetic bearings can remove contact point between rotor and supports by magnetic force. Controlling magnetic force is key point of this system to keep mechanical balancing of rotor. Due to high revolution speed of rotors, disturbances produce instability. The Disturbance Observer Algorithm (DO) is an approach to reduce effects of disturbances. The DO Controller was combined to generic PID Controller to improve performance of AMB. In DO, estimation mass is vital to enhance stability and accuracy of that. So, artificial neural network and iterative learning were hybridized to DO as intelligent techniques in estimation mass. Simulation assessment reveals iterative learning showed superior results in terms of accuracy and stability of AMB responses to the disturbances.