Adaptive Control of Multimodal Permanent Magnet Synchronous Motor Based on Embedded Neural Network

Abstract

Permanent magnet synchronous motors (PMSMs) occupy a core position in various industrial and household equipment due to their excellent energy efficiency, high-power density, and low-noise characteristics. However, facing dynamic factors such as load fluctuations, environmental temperature changes, and manufacturing tolerances, the stability and optimization of its operational efficiency have become a major challenge. To address these issues, this study innovatively proposes a multimodal adaptive control strategy based on embedded neural networks, aiming to enhance the adaptability of PMSM to complex working environments through intelligent learning and optimized decision-making. The core of the research is to construct a control system based on embedded neural networks, which can capture the characteristics of PMSM in different operating modes in real time and learn the corresponding optimal control strategy. The advantages of embedded neural networks lie in their compact model design and efficient computing power, making them highly suitable for resource-constrained motor control systems. Through multimodal learning, the system can recognize and adapt to various states of motor operation, such as start-up, acceleration, steady-state operation, and deceleration, thereby achieving precise control of motor performance.