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This paper presents a finite control set model predictive control (FCS-MPC) approach for two induction machines driven by a nine-switch inverter (NSI). In the traditional approach, two separate voltage source inverters are necessary to drive the independent induction motors. In the proposed method, the nine-switch inverter is used to control the separate motors with a reduced number of switching devices compared to traditional method. A robust control strategy that eliminates the interactions between separate mechanical loads is required to achieve a proper independent speed and torque control for two induction machines through the NSI. To ensure the reliability of the machine operation, the indirect-field oriented control-based model predictive control strategy is proposed. The proposed control strategy is experimentally validated across the 3.2 kW SiC-based NSI prototype. The control algorithm is performed on an Altera Cyclone IV Field-programmable gate array. The experimental results demonstrate that the proposed dual-model predictive control method provides a good and robust motor control operation under different loading conditions. Two induction motors are successfully controlled, and the independent speed and torque control are achieved. [ABSTRACT FROM AUTHOR]

This article proposes a new control strategy to regulate the six‐phase induction machine fed by a nine‐switch converter. The traditional model predictive control method is a favorable feedback control routine in variable‐drive applications. However, the lack of the term that assess the closed‐loop stability in the conventional cost function formulation may cause potential closed‐loop instability and be incapable of tolerating the parameter mismatch. Furthermore, the conventional model predictive control formulation cannot ensure global optimality in case of deviation from the nominal operating point. In this paper, the closed‐loop stability is enhanced by including a sliding mode stability term in the objective function. The traditional cost function is redeveloped as a sliding mode control stability term, and the selection of the control input that satisfies the closed‐loop loop stability condition is ensured. The robustness performance of the system is improved, and a better steady‐state performance is achieved. The proposed control strategy is proved using a hardware setup including a six‐phase 30° spatially shifted induction motor and nine‐switch converter prototype. The experimental results demonstrate that a secured six‐phase induction motor operation is guaranteed while improving the closed‐loop robustness. [ABSTRACT FROM AUTHOR]

Finite control set model predictive control (FCS-MPC) is an optimal control strategy that solves user-defined objective functions to determine the best control action for the next time interval. Real-time implementations of model predictive control techniques are quite challenging for certain topologies due to computation complexities. In this paper, key aspects of achieving robust, reliable, and efficient field programmable gate arrays (FPGAs) based model predictive control are presented for single-phase direct matrix topology. The effectiveness of FPGA-based model predictive control is validated experimentally using an ALTERA Cyclone IV FPGA. Experimental results show that an effective load current control performance is obtained by taking advantage of pipelining capability of the FPGA device. The tradeoff between control bandwidth, FPGA resources, and hardware utilization is discussed. [ABSTRACT FROM AUTHOR]