Model Predictive Control Of Permanent Magnet Synchronous Motor Based On Swarm Intelligence Optimization Algorithm

This thesis takes the Model Predictive Control（MPC）method of Permanent Magnet Synchronous Motor（PMSM）,which has attracted much attention in recent years,as the research object.Aiming at the problem of low steady-state control performance and large current pulsation existing in the current high-performance motor control of this method,an optimal two-vector model predictive current controller is proposed,which achieves a significant reduction in motor current pulsation during steady-state operation;Aiming at the problem that the MPC method is limited to the current loop application,this thesis expands the application of MPC in the speed control link,designs a model predictive speed controller,and constitutes an MPC cascade control system.This thesis also optimizes the system’s group intelligence algorithm to achieve the improvement of dynamic performance in the motor control process.The new cascade controller also has better processing capabilities for nonlinear constraint problems.Secondly,for the problem of low steady-state control accuracy of the traditional MPC method,this thesis points out that the reason is that only one voltage vector is output in a single cycle,resulting in large current pulsation.In order to solve this problem,a current loop control method based on the optimal two-vector finite set MPC method is proposed,so that the optimal voltage vector and the sub-optimal voltage vector can be output in one cycle to improve the dynamic and steady-state performance of the system.For the problem that only one voltage vector is output in a single cycle in the traditional MPC,which leads to low control accuracy,a current loop structure based on the optimal two-vector finite set MPC method is proposed to output the optimal voltage vector and the suboptimal voltage vector in a cycle,so as to improve the dynamic and steady-state performance of the system.For the problem of poor robustness and slow dynamic response of traditional PI controller,a speed loop MPC control method is proposed to replace the speed PI controller.The variable step-size swarm intelligence（SI）algorithm is designed to tune the controller parameters online.This method optimizes the weight coefficient through the swarm intelligence algorithm,so that the weight coefficient can be optimally set under any working condition.The complex weight coefficient tuning process is omitted and the speed controller has good anti-disturbance ability.In order to obtain the algorithm with the best optimization effect,this thesis compares the application of Glowworm Swarm Optimization（GSO）,Artificial Bee Colony Algorithm（ABC）and Beetle Antennae Search Algorithm（BAS）,and selects the algorithm with the best optimization speed.Finally,an experiment platform for PMSM screw slide table based on DSP28335 controller was built on the basis of simulation verification.The screw slide table is used to complete experiments such as sudden load and no-load start to verify the excellent dynamic response and anti-disturbance ability of the cascaded MPC method.