Research On The Measurement And Control System Of Cooking Robot Heat And Stir Fry

Due to the development of society,the standard of living of the broad masses of the people is also increasing.There is an old saying in China that"the people regard food as the heaven",so the quality of the people's demands for food production continues to increase.At the same time,Chinese cuisine is now seen by more and more people as the same kind of national character as the Beijing Opera,and it continues to receive attention throughout the world.However,the working pressure of people in the society is also increasing day by day.The society also urgently needs a robot that can complete automatic cooking to replace people to complete the cooking work.According to this demand,this paper designs the core part of the cooking robot-stir fry and heat.control system.Aiming at the current working requirements of cooking robots and the current status of control systems,the safety,stability and availability of cooking robots are fully considered.A parallel measurement and control system based on BP neural network to adjust PID controller parameters is studied to complete the cooking production of dishes.The main research contents of this article are as follows:(1)For the existing cooking robots,there is a poor real-time response,and problems such as unfamiliarity or overcooking of the foods after the production of the dishes are easily caused.In this paper,the parallel operation control mode of the frying system and the heat system is designed.The cooking activities are performed according to each other's motion information collected by the sensors,and the real-time and coordination of the frying system and the heat system are improved.(2)In-depth study of the process of Chinese cuisine production and the characteristics of maturity,construction of mature standards in the cooking automation process based on the exploration of the quality factors of the mature dishes,and to establish a mathematical model of the maturity of the dishes.(3)In-depth study of the BP neural network multi-layer feedforward training learning algorithm,combined with the construction of the previous chapter cooking automation maturity mathematical model,and according to the feedback information collected by the sensor through the BP neural network model algorithm learning to adjust the PID control The parameters of the device,so as to achieve the optimal control of the rotation of the motor,improve the working efficiency of the cooking robot.(4)In-depth study of the cooking robot's working characteristics,through the embedded control chip Cortex-M3 as the main control processor designed the corresponding motor drive control system and draw the control circuit schematic.At the same time,the human-computer interaction interface for cooking scenes was designed to meet the needs of the operators.