A radial basis function neural network based selfadapting predictive decoupling control system for gas collector pressure in coke ovens
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Abstract:
Pressure is measured at different levels in the loop layer because selfadapting predictive decoupling control systems are strongly coupled, disturbed, and nonlinear and there is a long time delay for gas collector pressure systems in coke ovens. By combing the traditional neural network control and proportional integral differential(PID) controllers based on radial basis function(RBF) neural network identification, the gas collector pressure is ensured to reach the desired technology range. The prediction model of an RBF neural network is used for advanced prediction of the actual output pressure to overcome delays in general gas collection. The simulation results and application indicate that the method can obtain ideal control results.
