The calibration of force offset for rocket engine based on deep belief network

Abstract

Background: Force offset is an important movement and control parameter in rocket motor development process, and its accurate measurement is a vital guarantee of rocket motor reliable operation, so there is an essential significance to achieve accurate force offset calibration. Methods: A novel force offset nonlinear calibration method is proposed based on deep belief network. Experimental platform is established and force offset calibration test is completed. Because the Levenberg -Marquardt process has the advantage of both Newton method and gradient descent method, test data are trained with Levenberg -Marquardt, decreasing nonlinear mapping convergence errors and realizing nonlinear calibration of force offset. Results and Conclusions: Training results show that the mean deviation rate of force offset after nonlinear calibration is less than 2.7%, better than the back-propagation neural network and least squares method, verifying the reasonableness and practicality of nonlinear compensation calibration method and effectively improving force offset calibration accuracy.