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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Finally, the capability of the proposed method is shown in the simulation results.
📌 3 Key Takeaways
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Objective & Hypothesis:This paper deals with the modeling 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.
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Key Findings & Data:The technique of feedback linearization and H1 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.
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Conclusion & Impact:Finally, the capability of the proposed method is shown in the simulation results.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
This paper deals with the modeling 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 H1 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.
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Cite This Article
KARIMI, H., BABAZADEH, A., MARALANI, P. J., MOSHIRI, B., PARSPOUR, N. (2022). Sensorless adaptive output tracking of transverse flux permanent magnet machines. ARI The Bulletin of the Istanbul Technical University, Cilt: 54 - Sayı: 3, 17–23. https://ajindex.com/articles/448130