Advance control of a synchronous reluctance motor drive
Publication Date
July 21, 2023
Creator
Abstract
This thesis investigates two predictive control algorithms designed to enhance the performance of a synchronous reluctance motor drive. In particular, a finite-control set solution approach has been followed. In particular, this thesis proposes the inclusion of integral terms into the cost function to ensure zero steady-state errors thus compensating for any model inaccuracy. In addition, a control effort term is also considered in the online optimization definition to achieve a quasi-continuous time digital controller given the high achievable ratio between the sampling frequency and the average switching frequency. After a comprehensive simulation study showing the advantages of the proposed approach over the conventional predictive controller solution over a wide range of operating conditions, several experimental test results are reported. The effectiveness of the proposed control approach, including a detailed analysis of the effect of the load and speed variations, is thus fully verified providing useful guidelines for the design of a direct model predictive controller of synchronous reluctance motor drives.
In addition, this thesis investigates an innovative duty cycle calculation method for a continuous-control set model predictive control. The formulation of the duty cycles, as well as the introduction of integral terms, enable good reference tracking performance with zero steady-state error at fixed switching frequency over the whole current operating range. Low current ripple with smooth and fast dynamics are achievable, making the proposed control algorithm suitable as a valid alternative in synchronous reluctance motor drives over the established control methods. Simulations and experimental results show the effectiveness and the advantages of the proposed control algorithm over the benchmark.
Item Type
ethesis
Thesis Type
PhD
Supervisors
Subjects (LC)
Associated Schools / Departments
Faculty of Engineering
eprints ID
73474
UoN Repository URI
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University_of_Nottingham_PhD_Thesis_Jacopo_Riccio_Final_Version.pdf
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