Efficient Continuous Control-Set Model-Free Predictive Control of a Grid-Forming Inverter With an LCL Filter

The application of finite control set model-free predictive control (FCS-MFPC) in power electronics eliminates the controller’s dependence on an explicit system model. However, high computational requirements hinder its practical implementation in power electronic systems. Furthermore, the inherent variable switching frequency of finite control set MFPC (FCS-MFPC) limits its suitability for high-performance applications, motivating the adoption of continuous control set MFPC (CCS-MFPC). This paper proposes a computationally efficient CCS-MFPC for regulating the output voltages of a grid-forming inverter (GFI) with an output LCL filter. The proposed controller incorporates constraints on the duty cycle and inverter-side filter current to ensure safe operation. The model-free strategy is based on an autoregressive structure with an exogenous input (ARX), enabling the estimation of GFI dynamics without the need for an explicit system model. Additionally, computational complexity is reduced by leveraging the structure of system constraints. Controller-hardware-in-the-loop (C-HIL) simulations are used to compare the proposed CCS-MFPC with existing CCS-MPC, FCS-MPC, and CCS-MFPC approaches. The results demonstrate the controller’s robustness to model inaccuracies, improved voltage tracking performance, and lower computational burden, paving the way for the practical application of MFPC in power electronic systems.