| Summary: | This article addresses the subject for bottom-following control of underactuated unmanned undersea vehicles (UUVs) with input saturation in the presence of unknown model uncertainties and unknown external disturbances. A robust adaptive dynamic surface bottom-following control scheme is developed based on the recursive sliding mode with nonlinear gains and neural networks, which can steer an underactuated UUV to precisely follow the bottom profile at a constant altitude as a basic feature. The bottom-following guidance law is derived based on the Serret-Frenet frame, the line of sight (LOS) and Lyapunov's direct technique. Then, the bottom-following controller is designed based on the recursive sliding mode and dynamic surface control (DSC), to stabilize the bottom-following errors. The radial basis function neural networks (RBF NNs) are employed to online approximate the uncertain dynamics of underactuated UUVs, while the adaptive laws are introduced to estimate the bounds of the RBF NN approximation errors and unknown environmental disturbances. Additionally, an auxiliary dynamic system (ADS) is presented to handle the effect of input saturation. The uniform boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis. The simulation results are presented to verify and illustrate the effectiveness of the proposed control scheme.
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