| In the servo drive system,the motor and the mechanical drive system are strongly coupled,time-varying,other nonlinear characteristics,which are typical electromechanical coupling systems.Mechanical and electrical parameters excitation will lead to coupling vibration of servo drive system,which seriously affects the reliable operation life of the system.Aiming at the shortcomings of low integration between mechanical system and electrical system in the modeling process of traditional electromechanical coupling system,a nonlinear multi-mass electromechanical coupling dynamic model is constructed.In order to solve the problem that the rapid suppression of system resonance is required by the drastic changes of speed and load under complex working conditions,a composite control method based on the combination of model predictive control and notch filter is proposed,which realizes the rapid suppression of mechanical resonance and the improvement of system robustness in the operation of servo drive system.The main research contents of this paper are as follows:(1)Dynamic model of multi-mass servo drive electromechanical coupling system is established.Considering the influence of the electromagnetic stiffness of the drive motor and the different stiffness parameters of the mechanical transmission system(shafting and gear),the lumped mass method is used to establish the dynamic model of the multi-mass servo drive electromechanical coupling system including the drive motor,single-stage spur gear,load motor and motor controller structure,which lays a solid theoretical foundation for the subsequent analysis of the dynamic characteristics of the system.(2)The dynamic response characteristics of servo drive system under the influence of different parameters are analyzed.Based on the established electromechanical coupling dynamic model of the multi-mass servo drive system,the numerical solution method is used to solve it.Combined with the nonlinear dynamic analysis theory,with the help of Matlab programming software,the time history diagram,phase plane diagram,Poincare section and amplitude-frequency response curve of the relative torsion angle varying with the electromagnetic stiffness,excitation frequency and meshing transmission error parameters in the mechanical transmission process are plotted.The influence on the variation of electromagnetic stiffness,excitation frequency and transmission comprehensive error on the nonlinear vibration characteristics of electromechanical coupling system is analyzed,which lays the foundation for the high-performance control of servo drive system.(3)A hybrid control algorithm combining model predictive control and notch filter is proposed.Aiming at the problem of mechanical resonance in servo system,the reason of introducing mechanical resonance point in the operation of multi-mass servo drive system is studied,and the influence of different stiffness parameters in mechanical transmission device on mechanical resonance of servo system is proved.The problem that PI control algorithm has insufficient adaptability to motor speed and current control and needs to determine many parameters is solved.The dynamic simulation mathematical model of servo system was established on the numerical simulation software platform,and a composite controller combining model predictive control and notch filter was proposed.The dynamic characteristics of servo drive system under two complex working conditions of sudden load and impact load were analyzed,and the vibration suppression effect of model predictive control algorithm was compared.(4)A verification bench for mechanical resonance vibration suppression test of digital servo system considering single-stage spur gear transmission is built.The experimental study on the electromechanical dynamic characteristics under sudden load and impact load was carried out,and the mechanical resonance suppression effect of the composite control algorithm combined with model predictive control and notch filter and the model predictive control algorithm was compared and verified.The correctness of the proposed composite control algorithm based on the combination of model predictive control and notch filter was verified. |
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