Model reference adaptive control using genetic algorithm and neural network for gas collectors of coke ovens

The pressure system of gas collectors of coke oven is a multivariable non-linear process. A model reference adaptive control using the genetic algorithm and the neural network for the pressure system of gas collectors of coke ovens is proposed in this paper. The neural model of the system is identified by the genetic algorithm. Another neural network is trained to learn the inverse dynamics of the system so that it can be used as a nonlinear controller. Because of the limitation of BP algorithm, the genetic algorithm is used to find the fitness weights and thresholds of the neural network model, and the simulation results testify that the model is satisfied and the control is effective.