Optimized Sliding Mode Control via Genetic Algorithm for Quad Tilt-Wing UAVs

In the ever-evolving landscape of unmanned aerial vehicles (UAVs), the focus of this paper is on enhancing the performance and control of a Quad-Titl wing UAV (QTWUAV). QTW-UAV, an electric-powered aircraft, offers the unique capability of vertical takeoff and landing (VTOL) akin to helicopters, as well as extended-duration horizontal flight similar to conventional airplanes. Employing a full dynamical model derived from the Newton-Euler formulation, the paper elucidates the development of a comprehensive flight control system. Sliding Mode Control (SMC) has been used on many different systems but the SMC gains are critical. On the other hand, the Genetic Algorithm (GA) is a good and optimum way to calculate hyperparameters. Our approach extends to SMC through the integration of GAs and the stabilities are proven with Lyapunov theory and for verification, the proposed controller is simulated. By bridging evolutionary computation with flight control, this research epitomizes the synergy between artificial intelligence and robotics, enhancing the applicability and robustness of quad tilt-wing UAVs in a dynamic and evolving landscape. This innovative work promises to significantly advance the field of UAV control systems and contribute to the development of intelligent, adaptable, and high-performance aerial vehicles.