To reduce the computational complexity of traditional model predictive torque control (MPTC) and improve the sensitivity of predictive control to disturbances, an improved three vector model predictive control strategy applied in permanent magnet synchronous motor (PMSM) is proposed. First, the principle of deadbeat synchronization between torque and flux linkage is adopted to reduce six candidate vectors in traditional torque prediction to two, and the cost function is designed to select the optimal voltage vector. In addition, disturbance observation compensation is introduced to compensate for the influence of load disturbance on the control performance of the predictive model. As experimental results show, the proposed three-vector model predictive torque control can obtain small torque ripple and current harmonics both in steady state and dynamic state.

When particle swarm optimization (PSO) is used to identify the parameters of permanent magnet synchronous motor (PMSM), the movement of particles is not selective, which makes the algorithm easy to fall into the local optimum, and the accuracy is poor. The simulated annealing particle swarm optimization (SAPSO) improves the accuracy and evolution speed, but SAPSO has redundant iteration problems. To solve these problems, a motor parameter identification method based on fast backfire double anneal particle swarm optimization (FBDAPSO) is proposed. By reducing the optimization time and quickly tempering and annealing the "misunderstood" difference, the motor adjustable model and fitness function are designed, and the number of iterations is constantly reset to achieve the effect of online identification. Under different working conditions, simulated and experimental results show that the proposed method can quickly and accurately identify the four parameters of the motor's stator, winding resistance, stator winding d-axis inductance, stator winding q-axis inductance, and permanent magnet flux linkage at the same time, compared with the traditional method of parameter identification, and it has better accuracy, rapidity, and robustness.

To address the unsatisfactory performance of particle swarm optimization (PSO), a novel multi-strategy self-optimizing simulated annealing particle swarm optimization (SOSAPSO) method for permanent magnet synchronous motor (PMSM) parameter identification is proposed. The full-rank mathematical model and the fitness function are developed. In SOSAPSO, the velocity term of the PSO is simplified and dynamic opposition-based learning (DOBL) is introduced in the inertia weight update process to avoid population monotonicity. Moreover, A Cauchy-Gaussian hybrid variation strategy based on similarity and density is devised to achieve self-learning in deep regions. Meanwhile, the simulated annealing (SA) with a memory and tempering mechanism is introduced into SOSAPSO, and the greedy optimization algorithm (GOA) is used to enhance local fine-exploitation capabilities when SOSAPSO evolution is stalled. The test results indicate the proposed method can effectively avoid local convergence problems and has better robustness and convergence speed.

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In this paper, three model predictive current control (MPCC) schemes for permanent magnet synchronous motors (PMSM) are studied. The first control scheme is the traditional optimal duty cycle model predictive current control (ODC-MPCC). In this scheme, according to the principle of minimizing the cost function, the optimal voltage vector is selected from the six basic voltage vectors which are optimized simultaneously with the duty, and then, the optimal voltage vector and its duty are applied to the inverter. In order to reduce the computational burden of ODC-MPCC, a second control scheme is proposed. This scheme optimizes the voltage vector control set, reducing the number of candidate voltage vectors from 6 to 2. Finally, according to the principle of minimizing the cost function, the optimal voltage vector is found from the two voltage vectors, and the optimal voltage vector and its duty cycle are applied to the inverter. In addition, in order to further improve the steady-state performance, another vector selection method is introduced. In the combination of voltage vectors, the third control scheme extends the combination of voltage vectors in the second control scheme. The simulation results show that the second control scheme not only reduces the computational burden of the first control scheme but also obtains steady-state performance and dynamic performance equivalent to the first control scheme. The third control scheme obtains better steady-state performance without significantly increasing the computational burden and has dynamic performance comparable to the first and second control schemes.