Design of fuzzy-CMAC neural-network complex controller for gas mixing process

The gas mixing pressuring and calorific value control of gas mixing process have the characteristics of strong coupling, nonlinear, uncertainty and large time-varying. If the calorific value of mixing gas is unstable in gas pressurization station, the quality and quantity of iron and steel production will be affected. In this paper, an complex decoupling control method of fuzzy-CMAC neural network is proposed according to the mixing gas pressure and calorific value decoupling control. the fuzzy decoupling controller is used to realize the initial adjustment of mixing gas and pressure, which has the characteristics of rapidity and anti-interference performance. The fine adjustment work of control process is completed by the secondary adjustment of CMAC-PID compound controller. The design of controller has the ability of parameter self-learning, and the simulation results show that this design scheme has a better dynamic performance than traditional PID scheme to verify the feasibility of this method.