| Summary: | 博士 === 大葉大學 === 電機工程學系 === 103 === Recently, the studies on the active magnetic bearing (AMB) has become more and more popular and practical. In some special environment, the magnetic bearing plays an important role to sole many problems such as noise, friction, and vibration for the conventional mechanical bearing. Nevertheless, the control of the AMB is another problem to solve. The magnetic force has a high nonlinear relation with the air gap. In practice, no precise mathematical model can be established because the rotor displacement in an AMB system is inherently unstable, and the relationship between the current and electromagnetic force is highly nonlinear.
This thesis proposes an intelligent control method for positioning an AMB system, using the emerging approaches of the Fuzzy Logic Controller (FLC) and online trained adaptive neural network controller (NNC), and self-tuning fuzzy Proportional Integral Derivative (PID) controller for current-control loop. An AMB system supports a rotating shaft, without physical contact, using electromagnetic forces.
In the proposed controller system, an FLC was first designed to identify the parameters of the AMB system. NNC uses an initial training data with two inputs signal (the error and derivative of the error), and one output signal obtained from the FLC. Finally, an NNC with online training features was designed using an S-function in Matlab software to achieve improved performance. The FLC and self-tuning fuzzy PID controller have been verified on a prototype AMB system. An experimental AMB system is implemented by real time windows target (RTWT) in Matlab environment. The system response proves a low overshoot, an exhibited zero steady-state error, and a reducing rotor displacement of an AMB system.
Keywords: Active magnetic bearing (AMB), fuzzy logic controller (FLC), neural network controller (NNC), self-tuning fuzzy PID controller.
|
|---|
