Research On LQR Control Of Maglev Bearing Based On Genetic Algorithm

Thanks to the progress of control technology and the continuous updating of materials science,electromagnetic bearing has been applied more and more in the field of some mechanical industrial production tools,such as various kinds of working masters,air pumps,synchronous generators,liquid helium pumps and other mechanical tools that use rotating output.As a kind of mechanical and electrical integration device which transforms the electric energy input coil into electromagnetic energy to provide support for the rotating shaft,the main components of active magnetic bearing include the rotating shaft,stator electromagnet,sensor,controller and power amplifier.The supporting function of the electromagnetic bearing is essentially based on the ability to change the electromagnetic force in the control link to offset the influence of the external force,which also makes the supporting characteristics of the electromagnetic bearing have both active and passive characteristics.In this paper,the one-dimensional mathematical model of the active magnetic bearing is studied firstly,and then extended to the four degree of freedom mathematical model.Finally,the mathematical model is sorted into dynamic equation for theoretical analysis.Then,the LQR control scheme is studied.On the basis of stable suspension,genetic algorithm is selected to optimize the control parameters.The LQR control and the principle of genetic algorithm are described in detail.Compared with the traditional LQR control,it is found that the LQR control method optimized by genetic algorithm is more excellent.The simplified control model can have excellent dynamic and static performance,and has strong anti-interference ability,which is very consistent Meet the experimental control requirements.The model of active magnetic bearing and its control system is established on MATLAB / Simulink platform to verify the feasibility of the control method.In order to verify the effectiveness of the model and the actual effect of the control system,a complete experimental platform is also built: according to the experimental requirements,an active magnetic suspension device is designed,including the shell,rotor and stator,which is manufactured and delivered by the factory;in order to monitor the movement of the rotor in real time,eddy current sensor is used to detect the movement of the shaft;hardware circuit is designed The control board processes the signals obtained by the sensor,adjusts the current of the stator coil of the active magnetic suspension device,so as to control the movement of the rotor.Finally,the experimental data are obtained,and the conclusion is obtained by analyzing the data.Through the establishment of the simulation model and the study of the model running results,it is found that the LQR control optimized by genetic algorithm has better anti-interference and stability than the traditional LQR control.Through the establishment of the experimental platform and the completion of the experiment,the experimental results are obtained.After comparison,it also shows the superiority of the optimized LQR control.