Data-Driven Model-Free Adaptive Control of Z-Source Inverters.

Area of Science:

• Electrical Engineering
• Power Electronics
• Control Systems

Background:

• Z-Source Inverters (ZSIs) are crucial for green energy but face stability challenges due to non-minimum phase characteristics and uncertainties.
• Model-based controllers struggle with parameter variations, unmodeled dynamics, and load disturbances, potentially compromising ZSI performance and stability.

Purpose of the Study:

• To propose a novel data-driven, model-free adaptive controller for Z-Source Inverters.
• To ensure robust stability and desired performance of ZSIs in the presence of system uncertainties and disturbances.

Main Methods:

• A two-step control strategy: first, updating the system model using real-time input/output signals.
• Second, re-tuning the control action based on the updated model for optimal performance.
• Lyapunov stability analysis to rigorously prove the convergence and stability of the proposed controller.

Main Results:

• The proposed data-driven adaptive controller effectively guarantees stability and performance for ZSIs under various uncertainties.
• Experimental validation demonstrates superior performance compared to traditional model-based controllers (PI, state feedback, LQI).
• The method shows significant improvements in key performance metrics.

Conclusions:

• The data-driven model-free adaptive controller offers a robust and effective solution for ZSI control challenges.
• This approach enhances the reliability and efficiency of ZSIs in green energy applications.
• The proposed controller presents a promising alternative to conventional model-based methods for power electronic systems.