| Polyphase permanent magnet synchronous motors have the advantages of high power density,high reliability,and small torque ripple,and are widely used in automotive,marine propulsion,aerospace and other industrial fields.Among them,the two-phase permanent magnet synchronous motor with a phase shift of 30°and the traditional three-phase motor have received extensiv e attention because of their similar structure and the advantages of multi-phase motors.With the rapid development of microprocessors,model predictive control,as an advanced control algorithm,is widely used in the fields of power converters and motor drives.In this paper,the surface-mounted dual-phase permanent magnet synchronous motor with a phase shift of 30° is taken as the research object,and its vector control strategy,model predictive current control strategy and model predictive control fault-tolerant strategy under phase failure are studied in depth.Simulation analysis and experimental verification of the above control strategy are carried out.Firstly,the mathematical model of a dual-phase permanent magnet synchronous motor in the natural coordinate system is examined in this study.The coordinate transformation matrix is derived based on the vector space decoupling theory.And the decoupling vector coordinate system is established.The six-phase voltage is examined.The voltage vector distribution of the source inverter is analyzed.On this basis,the vector control strategy based on the vector space decoupling coordinate transformation is analyzed.Secondly;in view of the long calculation time and large current harmonics of the model predictive current control strategy,a deadbeat optimization model predictive control strategy is proposed.Using the method of combining deadbeat prediction and model prediction,considering the one-beat delay compensation of the two,based on the principle of volt-second balance,the large vector and the middle vector in the fundamental wave sub-plane are selected to construct a synthetic virtual voltage vector as the vector control set.To simplify prediction models and reduce current harmonics,the deadbeat method is used to estimate the expected voltage position.And the number of vectors is reduced from 13 to 3 through the positioning sector,which reduces the computational burden of the system.The simulation results show that,compared with the finite set model prediction algorithm,the deadbeat optimization model prediction algorithm can achieve better dynamic and steady state performance.Additionally,the harmonic content of the phase current is lowered and the amount of computation required to verify the viability of the proposed technique is also reduced.Then,the suggested deadbeat optimization model predictive current control approach is extended to fault-tolerant control,aimed at the single-phase open circuit failure of the dual-phase permanent magnet synchronous motor.The model prediction algorithm is used to track the fault-tolerant reference current.The analysis of the fault-tolerant reference current after phase outage is analyzed.Based on the distribution of basic voltage vectors,an optimized virtual vector synthesis method is proposed.The fault-tolerant strategy of model predictive control based on simplified control set is analyzed.Finally,an experimental platform of dual-phase permanent magnet synchronous motor drive system based on TMS320F28335 is built.Based on theoretical analysis,the program of vector control and model predictive current control before and after improvement is written.And experimental verification and comparative analysis are carried out.The experimental results are consistent with the simulation results,which verify the feasibility of the above control strategy. |
