Adaptive structure control framework based on machine learning

A two-stage control framework is designed by using classification and regression methods of machine learning. Considering the pitch, azimuth motion and wind load of the antenna array, the sample conditions were established, and the training sample library was established. According to the different types of stage tasks, a variety of machine learning algorithms were selected to compare the prediction accuracy, so as to establish the prediction model respectively, and design the profile control framework. The evaluation set was established by random working conditions to evaluate the method of the control framework. The ACC of active control accuracy was more than 100%, and the control errors of the evaluation set were all less than 2%, demonstrating the effectiveness and feasibility of the adaptive surface control framework.