Trajectory tracking control of a morphing UAV using radial basis function artificial neural network based fast terminal sliding mode: Theory and experimental

Lately, Morphing Aerial Systems (MASs) have seen a surge in demand due to their exceptional maneuverability, flexibility, and agility in navigating complex environments. Unlike conventional drones, MASs boast the ability to adapt and alter their morphology during flight. However, managing the control and stability of these innovative and unconventional vehicles poses a significant challenge, particularly during the aerial transformation phases. To solve this problem, this manuscript proposes a Radial Basis Function Artificial Neural Network-Based Fast Terminal Sliding Mode Control (RBFANN-FTSMC) method. This approach is designed to effectively manage morphology changes, ensure precise trajectory tracking, and mitigate the impact of external disturbances and parameter uncertainties. Accordingly, the RBFANN-FTSMC will be evaluated against Proportional Integral Derivative (PID), Sliding Mode (SM), and Fast Terminal Sliding Mode (FTSM) controllers through two flight simulation scenarios to validate its effectiveness. Additionally, the control parameters will be optimized using a recent metaheuristic algorithm known as the Whale Optimization Algorithm (WOA). A novel hardware control diagram is explained. Finally, the ability to alter morphologies and the results of experimental tests are discussed to highlight the performance and limitations of the mechanical structure and the implemented RBFANN-FTSMC. •To address the control challenges of MASs, we proposed an intelligent method called Radial Basis Function Artificial Neural Network Based Fast Terminal Sliding Mode Control (RBFANN-FTSMC). To the best of our knowledge, the RBFANN-FTSMC is exploited for the first time on such a platform.•We presented a novel control scheme designed to manage flight transformation, dynamics changes, model asymmetry, over-actuation, and variations in geometric parameters.•We adopted a recent metaheuristic technique, specifically the Whale Optimization Algorithm (WAO), to optimize control parameters. This ensures that the parameters are set to their optimal values, thereby enhancing overall control effectiveness.•The ability to change morphologies and the results of experimental tests are discussed to illustrate the performance and limitations of both the mechanical structure and the implemented controller RBFANN-FTSMC.