Model-Free Predictive H∞ Control for Grid-Connected Solar Power Generation Systems
Author(s)
Chen, Jianmin
Yang, Fuwen
Han, Qing-Long
Griffith University Author(s)
Year published
2014
Metadata
Show full item recordAbstract
A novel model-free predictive mixed-sensitivity H∞ control scheme is proposed and applied to grid-connected solar power generation systems. The predictive sensitivity and the predictive complementary sensitivity are defined based on the predictive model. The model-free predictive mixed-sensitivity H∞ controller is derived from input/output measurements to achieve an optimal predictive mixed-sensitivity performance using a maxmin optimization method. Then, a simulation system for solar power generation systems is established using SimPowerSystems. Finally, the simulations are conduced to show the effectiveness of the proposed ...
View more >A novel model-free predictive mixed-sensitivity H∞ control scheme is proposed and applied to grid-connected solar power generation systems. The predictive sensitivity and the predictive complementary sensitivity are defined based on the predictive model. The model-free predictive mixed-sensitivity H∞ controller is derived from input/output measurements to achieve an optimal predictive mixed-sensitivity performance using a maxmin optimization method. Then, a simulation system for solar power generation systems is established using SimPowerSystems. Finally, the simulations are conduced to show the effectiveness of the proposed model-free controller, which outperforms the conventional proportional-integral and model-free linear quadratic Gaussian controllers in the tracking performance and the robustness of solar power generation systems.
View less >
View more >A novel model-free predictive mixed-sensitivity H∞ control scheme is proposed and applied to grid-connected solar power generation systems. The predictive sensitivity and the predictive complementary sensitivity are defined based on the predictive model. The model-free predictive mixed-sensitivity H∞ controller is derived from input/output measurements to achieve an optimal predictive mixed-sensitivity performance using a maxmin optimization method. Then, a simulation system for solar power generation systems is established using SimPowerSystems. Finally, the simulations are conduced to show the effectiveness of the proposed model-free controller, which outperforms the conventional proportional-integral and model-free linear quadratic Gaussian controllers in the tracking performance and the robustness of solar power generation systems.
View less >
Journal Title
IEEE Transactions on Control Systems Technology
Volume
22
Issue
5
Subject
Applied mathematics