Real-Time Experimental Study of Speed Control for PMSM Drive System on OPAL-RT Simulator Using Radial Basis Function Neural Network - Hoang, Xuan Hung; Tran, Thanh Hai; Than, Phan Minh; Ngo, Thanh Quyen; Nguyen, Van Sy; Le, Tong Tan Hoa (Association for Scientific Computing Electronics and Engineering (ASCEE), 2025)
This paper addresses the problem of improving speed control accuracy and disturbance rejection capability for Permanent Magnet Synchronous Motors (PMSMs), which are widely used in industrial applications requiring high-performance drives. Conventional controllers such as PID often exhibit limited performance under nonlinear and time-varying conditions. The sliding mode control combined with a Radial Basis Function Neural Network (RBFNN) is proposed to enhance robustness and adaptability to overcome these limitations. The main contribution of this study is the integration of an adaptive RBFNN to estimate and compensate for unknown disturbances in real time, ensuring precise and stable motor operation. The theoretical stability of the system is guaranteed based on Lyapunov’s theory. The proposed method is implemented in a MATLAB/Simulink environment and tested on the OPAL-RT OP5707XG real-time hardware platform. The control system includes a speed loop using the RBFNN and a current loop for field-oriented control. The motor is subjected to varying speed commands in three stages to evaluate performance under dynamic conditions. Simulation results show that the RBFNN controller significantly improves speed tracking accuracy, reduces overshoot, and adapts better to sudden changes compared to conventional PID control. Real-time experimental results further confirm the effectiveness of the controller, despite the presence of noise and hardware delays. Current control performance also demonstrates better torque production and phase symmetry under dynamic loading with the RBFNN. A comparative analysis between simulation and experimental data highlights the practical applicability of the proposed approach.