Stepped generalized predictive control of test tank temperature based on backpropagation neural network

Abstract

It is significant to ensure the temperature stability of the test tank, which has a direct impact on reducing production energy consumption, improving tank quality and ensuring experimental results. However, the high nonlinearity and long delay of the temperature control process in the test tank make it difficult to satisfactorily control the temperature by traditional control methods. To solve the problem, this paper designs a temperature prediction model for the test tank based on backpropagation neural network (BPNN), which has a good fitting ability for nonlinear systems. The proposed model was coupled with improved generalized predictive control (GPC) into a new test tank temperature control method, namely, BPNN-based stepped GPC. Simulation results show that the new control method could reduce the prediction error of the BPNN and effectively control the temperature of the test tank.