This paper investigates the attitude control problem for large-scale morphing unmanned vehicles. Considering the rapid time-varying and strong aerodynamic interference caused by large-scale morphing, a modified model-free control method utilizing only the system input and output is proposed. Firstly, a two-loop equivalent data model for the morphing unmanned vehicle is developed, which can better reflect the practical dynamics of morphing unmanned vehicles compared to the traditional compact form dynamic linearization data model. Based on the proposed data model, a modified model-free adaptive control (MMFAC) scheme is proposed, consisting of an external-loop and an inner-loop controller, so as to generate the required combined control torques. Additionally, in light of the aerodynamic uncertainties of the large-scale morphing unmanned vehicle, a rudder deflection actuator control scheme is designed by employing the model-free adaptive control approach. Finally, the boundedness of the closed-loop system and the convergence of tracking errors are guaranteed, based on the stability analysis. Additionally, numerical examples are presented to demonstrate the effectiveness and robustness of the proposed control scheme in the case of the effect of large-scale morphing.

This paper investigates the attitude control problem of the morphing vehicle subject to great dynamics changes and disturbances during the morphing phase. An improved model‐free adaptive control (MFAC) method is proposed based on the compact format dynamic linearization technique. Firstly, a compact format data model and data‐driven control scheme are established based on the input/output (I/O) data of the controlled plant, independent of the complicated and time‐varying mathematical model of the morphing vehicle. Secondly, a series of historical output data errors in a moving time window are introduced to the control law, in which the length of the moving time window can be adjusted according to the system order of controlled plant. The convergence and stability of the improved control law are then proved theoretically, which guarantees the convergence of the tracking errors and the boundedness of the input and output data. Finally, numerical simulations are presented to evaluate the proposed approach, and comparisons are made with the conventional proportional‐derivative control method. Simulation results demonstrate that the improved method possesses better effectiveness and robustness in the presence of model changes and disturbances.