Automatic Control of Food Machinery Based on Neural Network and Computational Torque Compounding

Abstract

In order to improve the effect of automatic control of food machinery, this paper combines neural network and computational torque compounding to study the automatic control system of food machinery to improve the effect of automatic control of food machinery. Moreover, this paper studies the problem of strong time-varying nonlinear friction disturbance in the servo motion system, and analyzes the friction nonlinearity at low speed and commutation motion of the system. In addition, this paper introduces the application requirements for the nonlinear friction compensation control method, and proves that the designed controller can maintain the stability of the system under the condition of strong nonlinear time-varying friction through the Lyapunov stability principle. The experimental study shows that the control strategy given in this paper can not only effectively improve the tracking speed of the joints, but also effectively alleviate the later tremor, and the automatic control effect of food machinery based on neural network and computational torque compounding has certain effects.