Experimental validation of model-free predictive control based on the active vector execution time for grid-connected system

Abstract

Abstract The application of Model-Free Predictive Control (MFPC) in power electronic systems has garnered increasing attention. In this paper, MFPC control based on replacing the classical factory model with an ultra-local model (ULM) is studied. Generally, the Integral Sliding Mode Observer (ISMO) is used to estimate the unknown part in the ULM where the non-physical factor in the ULM is selected with approximate values ​​ranging from the nominal value of the system which is contrary to the concept of MFPC control. In this research, an improved adaptive integral sliding mode observer (AISMO) based MFPC (AISMO-MFPC ) is proposed to estimate this factor with the unknown function in the ULM equation. The new observer design enables the estimation of this factor based on the current error, which allows for independent prposedcontrol of the system parameters.To obtain the lowest current ripple, the concept of active vector execution time (AVET ) has been incorporated into the proposed control where two vectors are selected in the sampling period to minimize the cost function instead of selecting a single vector. ULM is also used to calculate AVET which facilitates the implementation of the imposed control. The combination of the proposed AISMO-MFPC and AVET gives faster system response and reduces the current ripple and lower harmonics, especially in case of mismatch parameters. Finally, the effectiveness of the proposed control is confirmed under various conditions by the presented simulation and experimental results.

Keywords

• Model-free predictive control MFPC
• Finite states model predictive controller FS-MPC
• Ultra-local model ULM
• Adaptive integrated sliding mode observe AISMO
• Active vector execution time AVET
• Cost-function