Model Predictive Control (MPC) based on Discrete Space Vector Modulation (DSVM) has the advantages of simple mathematical model and fast dynamic response. It is widely used in permanent magnet synchronous motor (PMSM). Additionally, the control performance of DSVM-MPC is influenced by the accuracy of motor parameters and the select speed of optimal voltage vector. In order to identify motor parameters accurately, model predictive control for PMSM based on discrete space vector modulation with recursive least squares (RLS) parameter identification is proposed in this paper. Additionally, a method to preselect candidate voltage vectors is proposed to select the optimal voltage vector more quickly. The simulation model of RLS-DSVM-MPC is established to simulate the influence of different parameters on PMSM performance. The simulation results show that model predictive control for PMSM based on discrete space vector modulation with RLS parameter identification has a better control performance than that of without RLS parameter identification.

Three Hall-effect sensors can be used to estimate the continuous rotor position of permanent magnet synchronous motors (PMSM) with low cost. However, available position estimation methods generally require motor model parameters or have a phase offset. For this purpose, this paper proposes a harmonic-decomposition complex-coefficient filter-based phase-locked loop (HD-CCF-PLL) structure that is independent of motor parameters and has a near-zero phase offset. First, the triggering principle of Hall-effect sensors is introduced and the effect of Hall position error on current harmonics and torque ripples is analyzed. Then, coordinate transformation and harmonic decomposition methods are presented for three Hall-effect sensors. However, there is a limit to the number of times a microcontroller can perform real-time harmonic decomposition. The decomposed Hall position vector still contains high-order harmonics, which reduces position estimation accuracy. Therefore, an CCF-PLL technique is employed to filter out these harmonics and estimate the rotor position. An efficient method of CCF-PLL online parameter self-adjustment is also provided for adapting to different speeds. Finally, the effectiveness of the proposed method is verified by detailed experiments. The results show that the proposed method can effectively filter out the harmonics in the Hall position vector with higher position estimation accuracy, fewer current total current harmonic distortion, and smaller torque ripple than the existing method.

Direct torque control (DTC) is widely used in a permanent-magnet synchronous motor (PMSM), but it has its own shortcomings caused by high torque ripple. Deadbeat-direct torque and flux control (DB-DTFC) is a new torque and flux method compared with DTC. However, the traditional DB-DTFC is often based on rotor-flux-oriented control. The reference voltage of the stator is computed in a rotor-flux-oriented coordinate system, and the solution involves solving quadratic equations, which will increase the burden of computational processing. To improve the computation of the reference voltages and the control performance, this paper proposes a new DB-DTFC algorithm and introduces its basic principles. First, the proposed DB-DTFC algorithm uses the forward Euler equation to solve the reference voltage in a stator-flux-oriented coordinate system. Second, the discrete mathematical model is used to predict the next control current to achieve deadbeat control. Third, the structural model of the proposed DB-DTFC is constructed. Finally, the simulation model of the proposed DB-DTFC algorithm is built with a MATLAB/Simulink platform. The simulation results prove that the proposed DB-DTFC algorithm can achieve better control performance in torque and flux control compared with the DTC algorithm and SVM-based direct torque and flux control (SVM-DTFC) algorithm. In particular, the torque index of DB-DTFC is reduced about 6% in a limited speed range in comparison with the DTC algorithm.