In recent years, permanent magnet synchronous motors (PMSM) have gained widespread adoption in high-precision servo systems, such as robotics, aerospace, and astronomy due to their remarkable performance characteristics. However, their inherent nonlinearities and susceptibility to disturbances posed challenges in achieving optimal control. 

Researchers at the Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, have devised a groundbreaking fixed-time non-singular terminal sliding mode control (NFNTSMC) method integrated with an adaptive neural network (ANN) to address these issues and enhance PMSM performance. Published in the renowned journal ISA Transactions. 

PMSM systems, despite their advantages, are prone to parameter mismatches, external load disturbances, and magnetic field non-linearities. These factors significantly hinder the precision and robustness of traditional linear control strategies. Therefore, there is a pressing need for advanced control techniques that can handle these nonlinearities and disturbances effectively.

The research team began by designing a novel fixed-time non-singular terminal sliding mode control (FNTSMC) for nominal PMSM systems. This method offered fixed-time convergence, significantly improving the dynamic performance. However, in practical applications, the presence of parameter mismatches and external disturbances necessitated a high switching gain, leading to unwanted high-frequency chattering.

To address this challenge, the researchers incorporated an adaptive radial basis function (RBF) neural network into the control framework. This neural network efficiently approximated the unknown nonlinear disturbances in real-time, enabling the control system to compensate for them dynamically. As a result, the switching gain was reduced, minimizing sliding mode chattering.

The stability and convergence properties of the proposed control scheme were rigorously proven using the Lyapunov method. Simulations and experiments demonstrated the efficacy of the approach, showcasing its ability to achieve superior dynamic performance and robustness under various operating conditions.

The study presents several significant contributions to the field of PMSM control: Faster fixed-time convergence, a modified fixed-time stable system with a faster convergence rate than existing methods was proposed, ensuring rapid stabilization regardless of the initial system conditions. Non-singular control, a new continuous nonlinear piecewise function was designed to eliminate the singularity problem in the control law, ensuring smooth operation and unlimited control output. Adaptive disturbance compensation, an adaptive RBF neural network, whose weight adaptive law was derived from Lyapunov stability analysis, was employed to approximate and compensate for disturbances online, significantly reducing switching gain and sliding mode chattering.

The proposed NFNTSMC with adaptive ANN holds immense potential for enhancing the performance of PMSM-based servo systems across diverse applications. Its ability to handle complex nonlinearities and disturbances effectively translates into more precise and reliable operation, leading to improved overall system performance.

In conclusion, this innovative control strategy represents an important step forward in the field of PMSM control. By integrating advanced fixed-time control theory with adaptive neural networks, researchers have demonstrated a robust and efficient approach to achieving optimal performance in high-precision servo systems.