Robust Adaptive Control of Transverse Flux Permanent Magnet Machines using Neural Networks
| Authors: | Babazadeh Amir, university of Bremen, Germany
Karimi Hamidreza, University of Tehran, Iran (Islamic Republic of)
Parspour Nejila, University of Bremen, Germany |
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| Topic: | 4.2 Mechatronic Systems |
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| Session: | Mechatronic Control of Motors |
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| Keywords: | High gain observer, transverse flux permanent magnet machine, H_infinity control, RBF neural network, output tracking |
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Abstract
This paper deals with modelling and adaptive output tracking of a Transverse Flux Permanent Magnet Machine (TFPM) as a nonlinear system with unknown nonlinearities by utilizing High Gain Observer (HGO) and Radial Basis Function (RBF) networks. The technique of feedback linearization and H_infinity control are used to design an adaptive control law for compensating the unknown nonlinearity parts, such the effect of cogging torque, as a disturbance is decreased onto the rotor angle and angular velocity tracking performances. Finally, the capability of the proposed method is shown in the simulation results.
