Research On Modeling And Control Method Of Ceramic Shuttle Kiln Based On Deep Learning

The ceramic shuttle kiln is a crucial production equipment in the ceramic production process,and temperature control is crucial for the quality of ceramic products during firing.Up to now,the temperature control of ceramic shuttle kilns has mainly relied on PID control and manual assistance,with low levels of intelligence and less than ideal control effects,affecting the quality of the products.With increasing demand for the quality of ceramic products,it is urgent to study more advanced control methods for the temperature control of ceramic shuttle kilns.However,to study advanced control methods,it is necessary to establish its mathematical model.As the ceramic shuttle kiln is a nonlinear system with characteristics such as a large time delay and high coupling,it is difficult to develop an accurate mechanistic model,so this thesis proposes to establish a predictive model based on deep neural networks.Then,it studies the intelligent optimization control method of ceramic shuttle kiln temperature based on deep deterministic policy gradient,achieving satisfactory control of the ceramic shuttle kiln temperature and obtaining good control effects.The main work and innovation points of this thesis are as follows:(1)Based on thermodynamic theory,the temperature thermodynamic equation of the ceramic shuttle kiln is obtained,and an ideal heating curve of the ceramic shuttle kiln under appropriate conditions is fitted.The ideal heating curve serves as a reference for temperature control of the ceramic shuttle kiln.(2)A method for modeling the ceramic shuttle kiln using a deep neural network is proposed,which solves the problem of difficulty in establishing an accurate mechanistic model for the ceramic shuttle kiln.The modeling based on the GRU neural network is compared with that based on the traditional BP neural network and LSTM neural network.The experimental results show that the modeling based on the GRU neural network performs the best.(3)An intelligent optimization control method based on deep deterministic policy gradient(DDPG)is proposed for temperature control of the ceramic shuttle kiln.The system diagram of the ceramic shuttle kiln temperature control based on the DDPG algorithm is presented,and simulation experiments are carried out.The simulation results show that,compared with the ceramic shuttle kiln PID control,fuzzy control,and fuzzy PID control,the temperature control based on the DDPG algorithm increases the control accuracy by 28.5%,24.6% and 18.6% respectively,which fully verifies that the DDPG algorithm is effective and feasible for temperature control of the ceramic shuttle kiln.