Anti-disturbance and anti-saturation control of supercavitating vehicles

AbstractThe control design on the underwater motion of supercavitating vehicles is characterized by strong coupling of the cavity model and strong nonlinearity of the dynamic model. In order to consider the factors such as disturbance and actuation saturation that may occur during the practical application of the vehicles, the anti-disturbance controller is designed using the method of radial basis function neural network sliding mode control, and meanwhile a control synthesis scheme is proposed based on the method of restriction cycle optimization in this paper, so that the compensation of control force is achieved. The corresponding simulations are carried out to verify the performances of anti-disturbance controller and anti-saturation actuators. The results show that the anti-disturbance controller based on radial basis function neural network sliding mode can be adapted to the scenes of underwater and water-entry launch, and the anti-saturation actuators based on restriction cycle optimization gain better performances than the traditional sliding mode controller on trajectory and attitude tracking, which simultaneously avoids the occurrence of actuator saturation.