Model-Free Predictive Current Control for Voltage Source Inverter: A Comparative Investigation

This study presents a comprehensive comparative investigation between the Model-Free Predictive Control (MFPC) approach based on an Ultra-Local Model (ULM) and conventional Model Predictive Control (MPC) within the context of a two-level voltage-source inverter (2L-VSI). The MFPC strategy harnesses historical data and the ULM for enhanced adaptability in uncertain scenarios. Simulation results reveal that the MFPC approach, employing Algebraic Identification Approaches (API), Extended State Observer (ESO), and Integral sliding mode observer (ISMO), maintains commendable performance under steady-state conditions, even when encountering parameter mismatches. Notably, during quick changes in current reference, the MFPC approach, specifically MFPCC-ISMO, showcases superior dynamic performance. Furthermore, when contrasted with MFPC and conventional MPC methods, the ULM-augmented strategy exhibits notable superiority. The incorporation of the ULM substantiates the MFPC's efficacy, supporting robust power conversion systems conducive to the transition to cleaner and sustainable energy solutions.

• model-free predictive control
• voltage source inverter
• two-level inverter
• ultra-local model
• extended state observer
• algebraic identification
• current control
• parameter mismatch
• dynamic performance
• power conversion system

• 7 — Affordable and clean energy
• 9 — Industry, innovation and infrastructure

• Multilevel Inverters and Converters
• Advanced Control Systems Optimization
• Sensorless Control of Electric Motors
• model-free predictive control
• voltage source inverter
• two-level inverter
• ultra-local model
• extended state observer
• algebraic identification
• current control
• parameter mismatch
• Electrical and Electronic Engineering
• Control and Systems Engineering
• Engineering