Enhanced Model Predictive Control Using State Variable Feedback for Steady-State Error Cancellation

Area of Science:

• Electrical Engineering
• Control Systems
• Renewable Energy

Background:

• Predictive control algorithms offer fast dynamic responses but struggle with steady-state accuracy.
• Accurate steady-state tracking depends on precise system models and parameters.
• Uncertainties in system parameters, especially in photovoltaic (PV) applications, can lead to significant steady-state errors, impacting reactive power regulation.

Purpose of the Study:

• To present a predictive control scheme enhanced with integral state feedback for PV applications.
• To investigate the impact of parameter uncertainties on steady-state errors in PV systems.
• To analyze the robustness and sensitivity of standard predictive control versus the proposed enhanced controller.

Main Methods:

• Development of a predictive control scheme augmented with integral state feedback.
• Analysis of system robustness and sensitivity under parameter uncertainties.
• Simulation and experimental validation of the enhanced control approach.

Main Results:

• The proposed controller demonstrates improved steady-state reference tracking compared to standard predictive control.
• The integral state feedback effectively mitigates steady-state errors caused by parameter uncertainties.
• Both simulation and experimental results confirm the controller's effectiveness and robustness.

Conclusions:

• The enhanced predictive control with integral state feedback is effective for PV applications, particularly under parameter uncertainties.
• Integral state feedback is crucial for achieving accurate steady-state performance and reliable reactive power regulation in PV systems.
• The proposed approach offers a robust solution for improving the performance of predictive controllers in real-world applications.