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.