Predictive current control for induction motor using online optimization algorithm with constrains

Model predictive control (MPC) has become a hot research topic in applications. The combination of continuous control set MPC (CCS-MPC) and pulse width modulation (PWM) can achieve high-performance while overcoming the drawback of the uncertain switching frequency of finite control set MPC (FCS-MPC). Based on the Field-oriented control (FOC), this paper proposes the CCS-MPC current control method by adopting the Hildreth's quadratic programming procedure. The proposed method applies the augmented model and realizes the unified offset-free control of the d-axes and q-axes currents with on-line optimization. Tuning weighting coefficients of cost function is the key to MPC design; based on analyzing the effect of the weighting coefficients and prediction horizon length on location of the closed-loop system poles, this study presents a new method to determine these parameters. Experimental results demonstrate that the CCS-MPC current control method has good dynamic features and decoupling characteristics.