Precise Control and Speed Identification of Asynchronous Motors in AC Drive Systems

In AC drive systems, precise control and speed identification of asynchronous motors have always been significant challenges. To address this issue, our simulation system utilizes a Model Reference Adaptive System (MRAS) identification algorithm that estimates motor speed using easily measurable stator voltage and current signals. The algorithm implementation involves configuring reference and adjustable models where the error between their outputs drives an adaptation mechanism. However, the pure integration component in the voltage model of the speed identification algorithm causes error accumulation and drift problems. Therefore, we have developed an improved integral-type rotor flux estimation model to solve this issue, which enables more accurate motor speed estimation through modified integration techniques with compensation filters. The enhanced rotor flux estimation model effectively reduces error accumulation and drift problems while improving the accuracy of motor speed estimation. The model implementation incorporates low-pass filters and compensation networks instead of pure integrators, using transfer function blocks in Simulink to achieve better numerical stability. Furthermore, in our simulation system, we can further explore and optimize control parameters through systematic tuning algorithms and sensitivity analysis to enhance system performance and stability. Therefore, in future research, we will continue to investigate this problem deeply and further improve the existing simulation system using advanced control strategies like fuzzy logic or neural network controllers to meet evolving requirements and challenges.