Control of Leading-Edge Shock of Train Using Deep Neural Network to Prevent Unstart

The primary aim of this research is to create a comprehensive neural network model that can effectively regulate the position of the leading-edge shock in a scramjet by manipulating the required backpressure, thereby achieving, and maintaining hypersonic speeds. By utilizing computational fluid dynamic data, a dynamic model is constructed using a neural network-based approach to control the positions of the leading-edge shock train. The scramjet isolator, which is a duct where pressure increases from the inlet to the combustor via a series of shock waves, necessitates precise control of the leading-edge shock locations during scramjet operation. The model employed in this research project is a neural network adaptive controller implemented in MATLAB/Simulink software, which accounts for the nonlinear characteristics of the plant and predicts its future behavior. To enhance control performance, a robust controller is employed, integrating a learning rule that reduces the error percentage throughout the system's lifespan. The neural network is trained using flight behavior datasets, enabling it to learn from a set of training patterns. Plant identification is achieved through a neural network, capturing the system dynamics, and enabling the neural network to function as a controller. Additionally, the controller's performance is validated through simulations and optimization analyses. This research presents an adaptable, robust, and effective control system that provides added reliability and reduces disturbances.