Adaptive PI and RBFNN PID Current Decoupling Controller for Permanent Magnet Synchronous Motor Drives: Hardware-Validated Results

Abstract

This study presents an adaptive proportional-integral (PI) and radial basis function neural network proportional-integral-derivative (PID) current control solution for permanent magnet synchronous motor (PMSM) drives. The proposed controller includes four controls: a decoupling term, a PI term, a supervision term, and a radial basis neural network-PID (RBFNN-PID) term. The first control term makes up the nonlinear factors, the second automatically adjusts the control gains, the third guarantees the system stability, and the fourth optimizes the PID parameters to achieve optimal system performance. Unlike off-line tuned PID controllers, the adaptive controller includes an adaptive tuning method for the on-line adjustment of control gain based on the gradient descent strategy. Therefore, it can be adjusted and handle the uncertainty of any system parameters. The program is not only simple and easy to be implemented but also ensures the accuracy and rapidity of the tracking speed. The control system has proven to be asymptotically stable. To verify the theory and application of the algorithm, a comparative experiment between the adaptive PI + RBFNN-PID controller and traditional PI + PID controller was conducted, demonstrating good robustness, which can effectively improve the PMSM. The results confirm that the proposed design achieves excellent control stability (i.e., quicker transient responding and smaller steady state error) in the presence of parametric uncertainty compared to conventional PID methods.