In this study, nonlinear neural network controller will be developed to control plasma radial motion in Damavand Tokamak. It is essential to have a good model in order to design a proper controller for plasma radial motion. To achieve this goal, actuator circuits are simulated and in consequence based on simulator model and simulated actuator circuits nonlinear neural network controller will be designed in Damavand Tokamak. Comparison between neural network controller output and PD controller output shows the efficiency of proposed approach.

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am ini(a)ee. kntu. aG. ir m alivari(a)eetd. kntu. aG. ir Ahstract-A model-based fault detection method is developed using two Radial Basis Function (RBF) Neural Networks. Two RBF neural networks are used as process output models and process variables at normal conditions are used for training the networks. One RBF network estimates the process outputs with a positive error and the other one estimates the process outputs with a negative error for all training data. Extended Kalman Filter (EKF) algorithm is used to train neural network parameters. Outputs and variables of the penicillin fermentation simulator are used as practical data for testing the performance of the algorithm.