Research on nonlinear correction of capacitive suspension gap sensor
To address the issue of severe nonlinearity in the output characteristics of the capacitive suspension gap sensor,a hybrid kernel least squares support vector machine(HKLSSVM)model is proposed as the nonlinearity correction model. This model combines the advantages of the radial basis function and the polynomial kernel function. Furthermore,the coati optimization algorithm(COA)is employed to optimize the penalty factor and kernel function parameters of the HKLSSVM model. To validate the effectiveness of the model,the radial basis neural network model,the traditional LSSVM model,the particle swarm optimization(PSO)algorithm-HKLSSVM model and the COA-HKLSSVM model are used for nonlinear correction simulation analysis. The results show that the COA-HKLSSVM model shows the best correction precision and stability in the application of nonlinear correction of capacitive suspension gap sensor,and the linearity of the corrected capacitive suspension gap sensor is 0.43％,the root mean square error is 0.022 mm,and the maximum error is 0.068 mm,which meets the linearity requirements of the suspension control system for the suspension gap sensor.
capacitive suspension gap sensornonlinear correctioncoati intelligent optimization algorithm
万方数据
维普
