Review of Intelligent Motor Controller Parameter Self-Tuning Technology

The self-tuning of control parameters for permanent magnet synchronous motor controllers is extensively utilized in industrial and production settings. The self-tuning algorithms and strategies employed significantly influence the quality and efficiency of production processes. In response to diverse practical application scenarios and system performance requirements, scholars have developed numerous intelligent self-tuning schemes. This paper reviews a substantial body of recent research on intelligent self-tuning technologies for permanent magnet synchronous motor controller parameters conducted by international scholars. It summarizes typical intelligent self-tuning methods, including single-neuron proportional–integral–derivative controllers, neural network proportional–integral–derivative controllers, and proportional–integral–derivative controllers optimized using particle swarm optimized algorithms, and compares their performance metrics through simulation studies. Additionally, it outlines the self-tuning strategies and optimization improvements based on each intelligent algorithm, identifies key research challenges, and evaluates existing solutions. Finally, this paper provides an overview of the current state and future prospects of intelligent self-tuning technology for permanent magnet synchronous motor controller parameters.