Integrated Control Method for STOVL UAV Based on RBF Neural Network and Nonlinear Dynamic Allocation

A short takeoff and vertical landing unmanned aerial vehicle (STOVL UAV) is significantly influenced by factors such as the ship’s surface effect, deck motion, and jet effect during vertical landing on an aircraft carrier. The existing control logic cannot effectively solve the coupling problem of longitudinal attitude and trajectory, so it is hard to guarantee the stability and control accuracy of the UAV at low speed. To address the aforementioned interference and coupling problems, a comprehensive control law based on a radial basis function neural network (RBFNN) and nonlinear dynamic optimal allocation is designed in this paper. Firstly, the integrated landing control law of the STOVL UAV is designed. Considering the model uncertainty and complex landing environment, an RBFNN is used for online observation and compensation to improve the robustness of the system. Subsequently, a dynamic control allocation module based on nonlinear optimization is developed to simultaneously satisfy force and moment commands. The simulation results show that the integrated control method effectively decouples the pitch attitude and longitudinal trajectory at low speeds, resulting in effective convergence control of pitch angle, forward flight speed, and altitude. The integration of the RBFNN, as evaluated by the integral of absolute error (IAE), results in a 93% improvement in control accuracy compared to the integrated landing control law designed in this paper without the RBFNN integration.