State-of-Art, Development, and Challenges of Model-Free Predictive Control on Motor Drives

Model-free predictive control (MFPC) is an essentially robust strategy in motor driving systems, garnering significant attention and research. However, the existing literature lacks a comprehensive analysis of data-driven model design, a critical aspect that directly impacts prediction accuracy and control performance of MFPC. This paper innovatively categorizes MFPCs used in motor drives based on data-driven models, systematically investigating various model structures and updating algorithms, organizes and compares the characteristics of each model. In particular, the challenges faced by MFPC and explore potential future developments are delved deeply, presenting insights and perspectives that hopefully facilitate future research work in this area