Modeling and control of magnetic levitation system
Magnetic levitation technology had received more attention since it helps minimize friction due to physical contact. Example of engineering applications includes magnetic levitated vehicle, high speed bearings, and precision platform. Magnetic levitation system consists of electro-magnetic actuator...
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| Format: | Thesis |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/32283/5/NasrulRahmanAbdullahMFKE2011.pdf |
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| Summary: | Magnetic levitation technology had received more attention since it helps minimize friction due to physical contact. Example of engineering applications includes magnetic levitated vehicle, high speed bearings, and precision platform. Magnetic levitation system consists of electro-magnetic actuator where its supply current needs to be controlled in achieving equilibrium force in vertical position with a permanent magnet. Too much force inserted will pull the object towards the actuator. Its instability and non-linearity characteristics pose challenges in modeling and control of the system accurately. The objective of this study was to obtain mathematical model of a small scale magnetic levitation system using MATLAB. The system was excited with three MLS of PRBS signal. Parametric approach using ARX structure was used to approximate the model. The best model was accepted based on the best fit criterion and pole-zero analysis through SI toolbox. The result showed that lower order model the best; meanwhile higher order model exhibits noise characteristics. PID and LQR controller was designed for the model through the simulation. The result showed that PID controller provides better output than open-loop control. LQR controller exhibits faster response to the system with undesired transient error. The designed PID and LQR controller can be applied to the magnetic levitation system with further optimization. An implementation to real-time system would validate the result in simulation. Self-tuning or robust controller could be developed in future to increase the reliability of the controller. |
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