الفهرس | Only 14 pages are availabe for public view |
Abstract Automatic power stabilization control is the desired objective for any reactor operation, especially, nuclear power plants. A major problem in this area is inevitable gap between a real plant and the theory of conventional analysis and the synthesis of linear time invariant systems. In particular, the trajectory tracking control of a nonlinear plant is a class of problems in which the classical linear transfer function methods break down because no transfer function can represent the system over the entire operating region. There is a considerable amount of research on the model-inverse approach using feedback linearization technique. However, this method requires a prices plant model to implement the exact linearizing feedback. For nuclear reactor systems, this approach is not an easy task because of the uncertainty in the plant parameters and un-measurable state variables. Therefore, artificial neural network (ANN) is used either in self-tuning control or in improving the conventional rule-based expert system. The main objective of thesis to suggest an ANN, based selflearning controller structure. This method is capable of on-line reinforcement learning and control for a nuclear reactor with a totally unknown dynamics model. Previously, researches are based on back-propagation algorithm. Back-propagation (BP), fast back-propagation (FBP), and Levenberg-Marquardt (LM), algorithms are discussed and compared for reinforcement learning. It is found that, LM algorithm is quite superior. Three Mile Island (TMI), pressurized water reactor (PWR) is selected as nuclear power plant. The TMI model was developed, as well as proposed NN-controller model and the conventional proportional-integral derivative (PIO), controller model. All models are simulated under MATLAB/SIMULINK package. The method is based on a neural network model that embodies the nonlinear behavior of nuclear reactors and an iterative method to determine the one-step-ahead predictive control input. TMI modeling results either response to reference power variation or response to external reactivity variation concludes that, NN-controller using LM algorithm is the superior to conventional PIO controller. This means that, the suggested NN-controller has excellent robustness and performance features |