Neural network control of magnetic bearing

Abstract

The realization and capability of magnetic bearing principally depends on the design of controller. It is difficult to induce its precise mathematic model because the magnetic bearing has complex non-linearity. The classical PID control method focus on systems having precise mathematic models. The neural network control method does not need the precise mathematic model, and has entirely different information processing approach compared to the classical PID control. The neural network, based on the principles of self-adaptive and being-trained, has self-study capability, so it adapts to controlling a magnetic bearing system. In this paper, we simulate both the neural network PID control algorithm and the classical PID control algorithm with the disturbances of output force exist, and conclude that the neural network PID control is superior to the classical PID control in respect of adjusting time and overshooting values.