<span>This paper represents a test of a modified algorithm to minimize the cost function in the traditional finite control-set model predictive current control (FCS-MPC) to control the (five-leg) DC voltage input inverter. A Matlab/Simulink description of a system presents a certain deviation limits between the reference and the actual measured phase currents, also the model implements a load current limitation. The algorithm picks out a proper switching state, which makes the lower error value between the wanted and the prognosticated currents; the proposed technique sets the chosen switching state as a driving signal to the ten switches. The modified program eliminates the switching combination with error values above the requested ones. Thereafter the system response enhanced by lowering the overshoots. The rigidness of the model is examined by using a step change in reference signals.</span>
In this, work the finite control set (FCS) model predictive direct current control strategy with constraints, is applied to drive three-phase induction motor (IM) using the well-known field-oriented control. As a modern algorithm approach of control, this kind of algorithm decides the suitable switching combination that brings the error between the desired command currents and the predicated currents, as low as possible, according to the process of optimization. The suggested algorithm simulates the constraints of maximum allowable current and the accepted deviation, between the desired command and actual currents. The new constraints produce an improvement in system performance, with the predefined error threshold. This can be applied by avoiding the switching combination that exceeds the limited values. The additional constraints are more suitable for loads that require minimum distortion in harmonic and offer protection from maximum allowable currents. This approach is valuable especially in electrical vehicle (EV) applications since its result offers more reliable system performance with low total harmonics distortion (THD), low motor torque ripple, and better speed tracking.
The commonly reported measures of the predictive accuracy are evaluated in this paper. Absolute, squared, percentage, and integral errors methods are implemented, to reduce the objective function, which employed in model predictive control. These methods are usually investigated for dc source inverter, which controlled by finite set model predictive current control system, with three phase induction motor load. In this paper, the evaluation includes different aspects, accuracy, complexity, system harmonics content, and execution time. A vital criterion in this process is the performance of the inverter, and the matching between the reference and the measured machine currents. The evaluation shows that for one term objective function, absolute and square errors give similar results with less execution time for the absolute error, but if multi terms objective function the square error is better.
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