MODELING AND CONTROL OF GAS TURBINE COMBUSTOR WITH DYNAMIC AND ADAPTIVE NEURAL NETWORKS

This paper presents an Artificial Neural Network (ANN)-based modeling technique for prediction of outlet temperature, pressure and mass flow rate of gas turbine combustor. Results obtained by present modeling were compared with those obtained by experiment. The results showed the effectiveness and capability of the proposed modeling technique with reasonable accuracies of about 95 percent. This paper describes a nonlinear SVFAC (State Vector Feedback Adaptive Control) controller scheme for gas turbine combustor. In order to achieve the satisfied control performance, we have to consider the effect of nonlinear factors contained in controller. The controller is adaptively trained to force the plant output and to track an output reference. The proposed Adaptive control system configuration uses two neural networks, a controller network and a model network. The control performance of designed controller is compared with inverse control method and results have shown that, the proposed method has good performance for nonlinear plants such as gas turbine combustor. SVFAC technique is finally generalized for MIMO systems in this paper.