Deep Learning–Based Adaptive Flight Control for Nonlinear Aerospace Systems

Abstract

Deep learning (DL) has emerged as a fast expanding field of study in recent decades, redefining the state-of-the-art in a variety of methods, including speech recognition and object detection. Many projects in the fields of aircraft design, behaviour, and control rely on the extensive data-driven approach. These projects include the development of flight control systems, intelligent sensing, fusion-based prognosis and health management, and airliner flight safety monitoring. Aerodynamic nonlinearities, outside influences, and parameter fluctuations result in highly nonlinear, unpredictable, and time-varying dynamics for modern aerospace vehicles. Traditional robust and adaptive control methods frequently rely on permanent structures and simple models, which might restrict performance in situations where flying conditions change quickly. A deep learning-based adaptive flight control structure for nonlinear aircraft systems that learns and adjusts for unknown dynamic in real time is presented in this research. While an adaptive control rule guarantees closed-loop safety and trajectory tracking performance, a deep neural network is used to simulate modelling uncertainties and unmodeled nonlinearities. A nonlinear aeroplane model is used to test the suggested method under various aerodynamic circumstances and outside disruptions. The potential of machine learning for next-generation smart flight control systems is highlighted by simulation findings that show increased tracking accuracy, resilience, and flexibility when compared to conventional model-based adaptive controllers.