| Summary: | 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.
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