Towards bio-inspired control of aerial vehicle: Distributed aerodynamic parameters for state prediction

In an era where traditional flight control systems are increasingly strained by the demands of modern aerial missions, this research introduces a novel integration of bio-inspired sensing mechanisms into aerial vehicle control systems, aimed at revolutionizing the adaptability and efficiency of UAV operations. Current gust suppression technologies often activate only after disturbances have occurred, highlighting significant limitations in real-time responsiveness and computational efficiency. With a specific emphasis on employing distributed aerodynamic parameters for predicting flight states, the study utilizes a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network to investigate the predictive capabilities of these models under varying conditions, including scenarios with full and limited input data. The models were assessed on their ability to forecast the pitch rate of Unmanned Aerial Vehicles (UAVs), examining both the precision of predictions in response to different historical input sizes and their robustness against simulated sensor noise. Results highlight the potential of using aerodynamic data to enhance the reliability and adaptability of flight control systems, significantly reducing dependency on specific sensor inputs. This approach not only demonstrates the effectiveness of integrating sophisticated machine learning models with aerospace technology but also paves the way for more adaptive, efficient control systems in UAV operations.