# Optimization of Ship Permanent Magnet Synchronous Motor ADRC Based on Improved QPSO
## 1 Introduction  
The Permanent Magnet Synchronous Motor (PMSM) is widely utilized in ship propulsion systems due to its high power density, simple structure, easy maintenance, and convenient speed control capabilities [1]. However, the specific challenges of ship operations--such as complex load variations and environmental disturbances--place higher demands on motor control performance compared to other fields. Traditional PI controllers, which are simple and easy to implement, are commonly used in PMSM speed control systems. However, their limited disturbance rejection capability cannot meet the control accuracy and dynamic response requirements of ship propulsion systems under complex operating conditions [2]. To address these challenges, [PERSON], [PERSON] et al. [3] proposed a long-horizon model predictive control (MPC) method based on deep learning,which resolves the coupling and disturbance issues between the motor system and speed control terms. This method demonstrated superior performance compared to the PI controller. [PERSON], [PERSON] et al. [4] introduced a PI parameter adjustment and optimization approach based on multi-variable sliding-mode extremum seeking for PMSM speed control. By performing frequency domain analysis, an analytical formula for the controller parameters was derived, which enhanced the system's dynamic performance. [PERSON], [PERSON] et al. [5] designed an adaptive neural network-based control method to improve the position-tracking performance of PMSMs under uncertain system parameters and external disturbances. Although these methods can improve certain aspects of system performance, the complexity of these intelligent optimization algorithms makes them difficult to integrate with existing control systems, thus limiting their practical application. In contrast, the Active Disturbance Rejection Controller (ADRC) demonstrates exceptional disturbance rejection capabilities and high-precision control performance [6]. It effectively handles nonlinearities, uncertainties, and time-varying dynamics without the need for exact mathematical models. Moreover, ADRC simplifies the control process through custom-designed modules [7]. However, the nonlinear functions within ADRC have limitations. Specifically, they may lead to excessive system responses when dealing with large disturbances, resulting in insufficient nonlinear tracking performance. To address this issue, [PERSON], [PERSON] et al. [8] replaced the traditional fal function with a sigmoid function, which reduced system oscillations, although it remains limited when dealing with complex nonlinear systems. [PERSON], [PERSON] et al. [9] proposed a tuning scheme based on dynamic parameters, but it is difficult for researchers without practical experience to apply effectively. Over recent years, researchers worldwide have explored various methods to optimize traditional ADRC. [PERSON], [PERSON] et al. [10] proposed an ADRC method based on parallel multiple Extended State Observers (ESOs), which improved tracking performance. [PERSON], [PERSON] et al. [11] introduced a disturbance rejection ADRC (DR-ADRC) scheme to enhance the disturbance rejection capabilities of ADRC, analyzing the steady-state error of the disturbance channel transfer function and providing recommendations for improvement. [PERSON], [PERSON] et al. [12] embedded a discrete-time repetitive controller within the ADRC control law, which operates in parallel with the core discrete-time Extended State Observer (DESO). The repetitive controller compensates for AC disturbances, while the DESO suppresses DC disturbances, thus improving both the dynamic and steady-state performance of the current loop. The improvements made to ADRC mainly focus on the ADRC model itself. While these methods have enhanced the control performance of ADRC to some extent, [PERSON], [PERSON] et al. [13] provided a more systematic analysis of the principles behind ADRC, further exploring the impact of controller parameters on system stability and performance.