A Three-Stage Neural Network-Based Control Design for Chaos Synchronization in A Permanent Magnet Synchronous Motors (PMSM)

This paper investigates the detection and control of chaotic behavior in field-oriented control (FOC) systems for Permanent Magnet Synchronous Motors (PMSMs). Chaos, characterized by unpredictable and highly sensitive dynamics, can significantly impact the stability and performance of electrical machines. We begin by employing conventional methods, such as computing the Largest Lyapunov Exponent (LLE) and analyzing attraction basins, to distinguish between chaotic and periodic behaviors. Building on this foundation, we introduce a three-stage neural network (NN)-based control design for chaos synchronization, leveraging unsupervised learning (UL) to exploit the hidden properties of chaotic systems without explicit supervision. By integrating clustering, dimensionality reduction, and unsupervised modeling techniques, we demonstrate the potential to efficiently synchronize chaotic behavior in PMSMs. This approach not only enhances the understanding of chaotic dynamics but also enables the design of a robust NN-based control strategy. The proposed methodology highlights the synergy between artificial intelligence (AI) and chaos theory, offering powerful tools for analyzing and controlling chaotic systems. Our findings pave the way for robust applications in complex industrial environments, where chaos synchronization can improve the reliability and efficiency of electrical machines. This study underscores the transformative potential of AI-driven techniques and the three-stage neural control framework in advancing the control of chaotic systems and their practical implementation in real-world scenarios.