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Experimental validation of minimum cost function-based model predictive converter control with efficient reference tracking

Experimental validation of minimum cost function-based model predictive converter control with efficient reference tracking

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This study proposes a robust and powerful finite control set-model predictive control (MPC) algorithm to control the load current with lower total harmonic distortion and efficient reference tracking. In this control, the cost functions are determined for all the possible switching states of the converter and a switching state is selected corresponding to the minimum cost function for actuating the converter in the next sampling time period. To justify the performance of the proposed MPC scheme, a comprehensive study with the carrier-based pulse-width modulation, hysteresis current control and proposed minimum cost function-based MPC of the three-phase load current has been verified in MATLAB Simulink as well as the validation with dSPACE RTI1104 experimentation. This study validates the robustness of the proposed minimum cost function-based MPC control with a RL-load and three-phase induction motor (IM) load. The simulation and experimental results justifies the proposed MPC algorithm with potential tracking of the RL-load current corresponding to the reference current with lower harmonic contents as well as tracking of predictive torque and flux compared to the nominal torque and reference flux of IM, respectively.

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