Adaptive Output Tracking of Transverse Flux Machines Using Neuro-Fuzzy Approach

This paper deals with adaptive output tracking of a transverse flux permanent magnet machine as a nonlinear system with unknown nonlinearities by utilizing Takagi-Sugeno type neuro-fuzzy networks. The technique of feedback linearization and H control are used to design an adaptive control law for compensating the unknown nonlinear parts, such the effect of cogging torque, as a disturbance on the rotor angle and angular velocity tracking performances. Finally, the capability of the proposed method is shown by the simulation results.