2015 34th Chinese Control Conference (CCC) 2015
DOI: 10.1109/chicc.2015.7260890
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A three-phase voltage source PWM rectifier with strong DC immunity based on model predictive control

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Cited by 5 publications
(2 citation statements)
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“…A capacitive energy-storage feedback-control strategy with a current inner loop and DC-side capacitive energy storage as the outer loop was presented [22] to overcome slow outputvoltage regulation when the load changes and to improve the dynamic characteristics of the system. Similarly, a different control strategy with voltage squared as the outer loop and model-predicted current as the inner loop was designed to address the same problem [23]. The results of the latter study reinforced the fast-tracking ability of the system to DC output voltage.…”
mentioning
confidence: 80%
“…A capacitive energy-storage feedback-control strategy with a current inner loop and DC-side capacitive energy storage as the outer loop was presented [22] to overcome slow outputvoltage regulation when the load changes and to improve the dynamic characteristics of the system. Similarly, a different control strategy with voltage squared as the outer loop and model-predicted current as the inner loop was designed to address the same problem [23]. The results of the latter study reinforced the fast-tracking ability of the system to DC output voltage.…”
mentioning
confidence: 80%
“…Using the simplified model of the PWM rectifier in [16], the internal model controller can improve the robustness and dynamic-response ability of the system, but its control effect is largely limited by the accuracy of the model. In [17], the performance is increased by introducing the model predictive control to the double closed-loop control, but its computation is so large that this method is not suitable for engineering applications. Considering the nonlinearity of the model and the parametric uncertainties, a robust voltage tracking algorithm with variable sliding surface is presented to ensure the robustness of the system [18], however, the algorithm has strong adaptive ability but high complexity.…”
Section: Introductionmentioning
confidence: 99%