Research On Finite Set Model Predictive Control And Prediction Error Suppression Of Permanent Magnet Synchronous Motor

Permanent magnet synchronous motors(PMSMs)play an increasingly important role in modern industry,with increasingly high demands on their control performance.To continuously improve the control performance of PMSMs,many scholars have proposed various advanced control theories.Model predictive control(MPC),the approach,characterized by its simple modeling and rapid dynamic response,has gained widespread recognition and adoption.This control method achieves optimal control of the motor by predicting the system model,thereby improving the motor’s performance and efficiency.In this thesis,finite set model predictive control(FCS-MPC)is used to study PMSMs.Corresponding solutions are proposed for delay,large computation load,and model mismatch problems.Specifically,this thesis combines the traditional FCS-MPC with the mathematical model of current control for PMSMs and proposes an FCS-MPC algorithm suitable for PMSMs,providing the corresponding predictive model and cost function.Finally,the performance of the algorithm is verified through simulations and experiments,demonstrating its effectiveness and feasibility in improving the control performance of PMSMs.Furthermore,various delays in sampling,computation,and dead zone problems of inverters that exist in practical situations are analyzed in detail to determine their impact on the performance of FCS-MPC.Then,the thesis proposes a new cost function design for multi-step predictive control to solve these issues,mainly improving the steadystate performance of traditional FCS-MPC.Finally,simulation verification of the multistep predictive control performance is conducted.Next,to address the problem of large computation load and difficult practical application faced by traditional multi-step predictive control proposed in the previous chapter,a multi-step predictive control method based on discrete-time sliding mode is proposed.This method can effectively solve the problem of excessive computation load in traditional multi-step predictive control and improve the anti-load disturbance ability of the PMSM control system.The RTU-BOX-based motor experimental platform is introduced to experimentally verify the control performance of this algorithm.Finally,the sensitivity analysis of model predictive control parameters is carried out,and the results show that the stator inductance mismatch has the most significant impact on predictive control.To address parameter mismatch,an adaptive supertwisting state observer is designed to improve the parameter adaptability of model predictive control and enhance the robustness of the entire controller.