Research on Neural Network-Assisted Input Optimization in Control System Frameworks

The development of automatic guided vehicles is the most important research topic. Deep learning has become increasingly important in the field of automatic control in recent years. In general, deep learning is used to tune controller parameters for better optimization. Control system designers still have to process sensor data and input reasonable control quantities. The paper presents an analysis of the characteristics of a single inductor and adopts manual data fusion methods, network regression effects, and embedded neural network deployment environments while applying the neural network model to the input link of the automatic control system. In this experiment, low-cost inductor array data is regressed to the lateral deviation provided by the image processing algorithm through a neural network model. The neural network model can effectively replace manual methods to process low-cost multidimensional sensor data, thereby reducing the time required to design automatic control systems.