Identification and control of boiler combustion system based on neural networks and ant colony optimization algorithm

Due to large delay time, varying coal's quality and steam load, boiler combustion system was difficulty controlled. Nonlinear system's delay time must be well identified. The abrupt mutation result from the training error sum square of the real output and the expected output of the neural network was used to identify the delay time. The input sample period of the neural network was changed so that it could discriminate the delay time of the nonlinear model. The discriminated large time-delay was applied to neural network prediction model. The errors between input and prediction model output were used to search PID controller parameters based on ant colony optimization algorithm. The method was applied to control boiler combustion system. The simulation results show that this scheme has much better advantage of celerity and robustness.