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Model predictive control (MPC) is emerging as a powerful control method for the high performance control of permanent magnet synchronous motor (PMSM) drives due to its merits of simple principle, quick response, and flexibility to handle multiple variables and constraints. However, conventional MPC applies only one voltage vector during one control period to minimize the cost function, which produces relatively high steady-state ripples and high computational burden due to the enumeration-based predictions. Introducing duty cycle control into MPC can improve its steady-state performance, but the control complexity is further increased. This paper proposes a generalized multiple-vector-based MPC for PMSM drives, which unifies the prior MPC methods in one frame with much lower complexity and computational burden by eliminating the enumeration-based predictions and complex calculations in conventional MPC methods. This is achieved by reconstructing the three-phase duties obtained from the classical deadbeat control with modulator, which also reveals the inherent relationship between deadbeat control and the proposed MPC methods. The presented experimental results confirm the effectiveness of the proposed method. Index Terms-AC motor drives, current control, predictive control, synchronous motor drives. I. INTRODUCTION T HE permanent magnet synchronous motor (PMSM) drives are receiving more and more attention in the industry applications due to the merits of high torque/power density, high efficiency, and high reliability [1]. In the past, vector control (VC) and direct torque control (DTC) are the two conventional high performance control methods for PMSM drives [2], [3]. Recently model predictive control (MPC) is emerging as a powerful alternative to conventional VC and DTC [4]-[8]. It eliminates the current regulators and PWM block in VC and

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