研究目的
To propose an adaptive radial basis function neural network fuzzy control scheme to enhance the performance of shunt active power filter (APF).
研究成果
An adaptive RBF NN fuzzy controller is successfully applied in three-phase shunt APF. The simulation results confirm the effectiveness of the proposed controller, illustrating that the APF system based on the proposed method has outstanding compensation performance and strong robustness.
研究不足
The technical and application constraints include the need for careful selection of controller parameters to achieve the goal that compensation current can track instruction current. The simulation does not account for all possible real-world variations and disturbances.
1：Experimental Design and Method Selection:
The RBF NN is utilized on the approximation of nonlinear function in the APF dynamic model. The sliding mode control term is adjusted by adaptive fuzzy systems to compensate the network approximation error and eliminate the existing chattering.
2：Sample Selection and Data Sources:
Simulation study using Matlab/Simulink package with SimPower Toolbox.
3：List of Experimental Equipment and Materials:
APF system parameters including inductance and resistance of the APF, AC supply voltages, and switching function.
4：Experimental Procedures and Operational Workflow:
The control strategy involves sliding mode control, adaptive RBFNN controller, and adaptive fuzzy gain controller. The RBFNN is used to approximate the nonlinear function in APF dynamic model, with weights adjusted online.
5：Data Analysis Methods:
The effectiveness of the proposed controller is confirmed through simulation results, demonstrating outstanding compensation performance and strong robustness.
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