An improved model predictive control for permanent magnet synchronous motor drives

Abstract

Model predictive control (MPC) has been applied in permanent magnet synchronous motor (PMSM) drives as a powerful control method due to its conceptual simplicity and flexibility to incorporate nonlinear constraints. However, in the conventional MPC, only one vector is applied during one control period, which produces large torque and flux ripples and high current harmonics. Recently the zero vector was introduced as the second vector in one control period to obtain steady state performance improvement. However, the fixed zero vector may not be optimal for the aim of error minimization. To solve this issue, this paper proposes an improved two-vector-based MPC, in which the second vector is not limited to an zero vector, but relaxed to an arbitrary vector. The simulation and experimental results prove that, the proposed MPC presents better steady state performance than conventional MPC while the quick dynamic response is maintained.