Research On Position Prediction Method Of Linear Actuator Based On Support Vector Machine

Modern linear motion devices are widely used and can convert electrical energy into mechanical energy to achieve linear motion.Compared with rotary motion,they have the advantages of simple structure and low energy consumption.In order to achieve a better shift control effect,the direct drive shift system using an electromagnetic linear drive device needs to obtain position and speed information as the feedback of closed-loop control.Speed information can be obtained by motor or engine,and position information is measured by displacement sensor.The existence of displacement sensors has led to a sharp rise in cost,and the structural design has become complicated.In order to eliminate the limitation of the sensor,the position prediction technology of the displacement-free sensor is studied.Support vector machine?SVM?based on structural risk minimization is often used to solve small sample,non-linear,high-dimensional recognition and regression prediction problems.It can be applied to direct drive shift system position prediction.In this paper,direct-drive system problems shift position predicted by theoretical analysis,simulation and experimental validation of the method,the establishment of a shift position prediction model based on support vector machine algorithm.For standard support vector machine for noise sensitive defects,proposed and applied least squares support vector machine algorithm based on fuzzy membership function.Training and testing the model by collecting the training data,using a direct drive gear shifting system for test stand experiments.The specific work includes the following aspects:?1?Design and analyze the structural form and operating principle of the direct drive shift system.The structure and operating mechanism of the electromagnetic linear drive are introduced,and the structure and principle of the direct drive shift system are described.A mathematical model of the shift process of the direct-drive shift system is established,and the shift control strategy is given based on the mathematical model of the shift.Finally,the structure and control system of the test bench of the direct drive shift system are introduced.?2?Completed the sensitivity analysis of the characteristic parameters during the operation of the direct drive shift system,and determined the data set required for model training.Based on the back-EMF method,the relationship between the operating characteristic parameter and the shift displacement is established;Using the sensitivity analysis of the sobol method based on sobol sequence sampling,the sensitivity between the operating characteristic parameters and the shift displacement is obtained.Determining the training parameter sensitivity analysis,using a direct drive gear shifting system test bench collect sufficient training and test sets.?3?The support vector machine algorithm is combined with the electromagnetic linear drive device,and successfully applied to shift position prediction.Firstly,the structural risk minimization and the principle of support vector machine algorithm are introduced,and a support vector regression model is established.The training set collected by the test bench of the direct drive shift system is used for training to obtain a position prediction model based on support vector machine.Location prediction model using the test set of training data to verify,and analyze the results.The simulation results show that the accuracy of the position prediction model for the shift position prediction is more than 96%,the root mean square error?RMSE?is less than 1,R-squared is more than 0.97,the prediction time of a single data is less than 0.005ms.Position prediction error basically meets shifting requirements.?4?A least square support vector machine?LSSVM?based on fuzzy hypersphere membership function is applied to shift position prediction,which reduces the noise sensitivity of the prediction model.Firstly,the basic principle and prediction model of fuzzy least squares support vector machine algorithm are introduced;the training set and test set data are used to verify the least squares support vector machine position prediction model and the results are analyzed.It can be obtained through the training and prediction of multiple working conditions.The prediction accuracy is above 98.5%,the root mean square error?RMSE?is less than 0.5,the R-squared is above 0.99,and the prediction time of a single data is within 0.02ms Down time is longer.Taking voltage 30V and speed difference 450 r/min as an example,the accuracy of this method is improved by 2.07%and the prediction error is reduced by 48.4%compared to standard support vector machines.?5?Experimental verification of the position prediction model based on support vector machine.The direct-drive gearshift system test bench was used to perform shift experiments to verify the effectiveness of the position prediction model.The experimental results showed that the maximum prediction error was 5.9mm.The value of one shock during operation was4.5m?s^-3lower than the allowable one shock.Value,the shift can be realized smoothly under the condition that the shift shock is guaranteed.