Recurrent Neural Network-Based Robust Adaptive Model Predictive Speed Control for PMSM With Parameter Mismatch
Ty Trung Nguyen, Hoang Ngoc Tran, Ton Hoang Nguyen, Jae Wook Jeon
Abstract
Due to the fast dynamic response and ability to handle the constraints, model predictive control (MPC) is becoming an exciting and widely applied approach for permanent magnet synchronous motor. However, the control performance of a conventional MPC is significantly affected by the model parameter mismatches. In addition, the high computational volume is also a limiting factor of MPC. This article <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\prime }$</tex-math></inline-formula> s main objective is to solve these problems by proposing an advanced control structure. First, the parameter mismatches are estimated online by a discrete-time mechanical parameter observer, then a robust adaptive model predictive speed control (RA-MPSC) is designed to suppress the influence of parameter mismatches. Secondly, a recurrent neural network based algorithm is introduced to compute the RA-MPSC control law by solving an optimization problem in real-time. Lastly, the simulation and experimental results are presented to validate the performance of the proposed method.