Research On Predictive Control Of Permanent Magnet Synchronous Motor For Self-Propelled Artillery

With the continuous advancement of national defense and the military modernization,the national defense undertakings of all countries in the world are also greatly improved.As a powerful fire suppression and fire support weapon,self-propelled artillery is the focus of national defense research and development.The servo system of the self-propelled artillery is the executive part of the artillery,which plays a key role in accurately striking the target.Therefore,the research on self-propelled artillery servo system is of great significance.In order to improve the firing accuracy and damage probability of self-propelled artillery better,the motor in the servo system needs to meet the characteristics of large speed regulation range and good dynamic and static performance.Because of its high power density and fast response speed,permanent magnet synchronous motor has been widely used in different servo control fields,and can well meet the needs of self-propelled gun servo system.Therefore,the in-depth research on the control technology of permanent magnet synchronous motor plays a great guiding role in improving the performance of self-propelled artillery in all aspects.In this thesis,the control system of permanent magnet synchronous motor is deeply studied,mainly including control mode analysis,parameter identification,sensorless control analysis and the construction of the actual system.The main work is as follows:(1)Permanent magnet synchronous motors(PMSM)with different rotor structures are introduced.The mathematical model of three-phase PMSM in synchronous rotating coordinate system is established.In addition,the coordinate transformation principle is briefly explained.Finally,the principle of vector control is introduced,and the PMSM double closed-loop vector control system is analyzed.(2)On the basis of vector control,the finite set predictive controller is used to replace the traditional PI controller.In view of the difficulty in selecting the weight coefficient of cost function in traditional finite set predictive control,different weight coefficients are selected for simulation analysis,and the cost function optimization method based on online neural network is designed.Finally,the feasibility of optimizing the weight coefficient of cost function based on online neural network is simulated and verified.(3)In order to solve the problem that the model predictive controller is more dependent on motor model parameters and easily causes parameter mismatch during motor operation,the motor parameters of permanent magnet synchronous motor are identified by model reference adaptive(MRAS)online parameter identification method.And simulation verification is carried out.(4)In view of the problem that the position sensor is difficult to maintain after being damaged in the artillery field combat environment,and its own weight will also affect the performance of the motor.The adaptive sliding film observer without position sensor is studied.The convergence speed of the rotor angular velocity waveform identified by the traditional adaptive synovial observer is slow,so the digital phase-locked loop is added to optimize the parameter identification of the adaptive synovial observer,and the feasibility of the algorithm is simulated and verified.(5)Draw the PCB diagram of the main control board and drive board of the hardware part of the system,and punch and solder the board.Research on software programming on the built hardware system.