Optimized sliding surface predictive control of a voltage source inverter with improved steady-state performance.

Area of Science:

• Power Electronics
• Control Systems Engineering

Background:

• Model Predictive Control (MPC) is widely used in power electronics for energy conversion.
• Analyzing MPC stability is complex, often requiring Lyapunov-based methods due to its nonlinear nature.
• Standard frequency-domain tools are unsuitable for MPC closed-loop stability analysis, and improper objective function selection (e.g., ℓ₁ norm) can lead to instability.

Purpose of the Study:

• To propose a novel stability-guaranteed objective function design for MPC in three-phase voltage source inverters.
• To enhance the reliability and performance of power electronic systems using advanced control strategies.
• To address the inherent stability challenges associated with MPC.

Main Methods:

• Developed an optimized sliding surface predictive control strategy.
• Reformulated the MPC objective function as a sliding surface function, integrating sliding-mode control (SMC) theory.
• Selected the optimal input based on switching combinations satisfying SMC stability criteria.

Main Results:

• The proposed method ensures closed-loop stability, overcoming limitations of traditional MPC objective functions.
• Experimental validation confirmed the effectiveness and potency of the developed control strategy.
• Achieved improved system performance compared to designs that do not prioritize stability.

Conclusions:

• The proposed stability-guaranteed objective function design procedure effectively enhances MPC for three-phase voltage source inverters.
• Integrating SMC principles into MPC design provides a robust solution for stability assurance.
• The validated strategy offers a reliable and high-performance control method for power electronic applications.