Composite learning adaptive sliding mode control for AUV target tracking

This paper studies the controller design for an autonomous underwater vehicle (AUV) with the target tracking task. Considering the uncertainty the nonlinear longitudinal model, a sliding mode controller is designed. Meanwhile the neural networks (NNs) are used to approximate the unknown nonlinear function in the model. To improve the NNs learning rapidity, the prediction error which reflect the learning performance is constructed, further the updating law is designed utilizing the composite learning technique. The system stability is guaranteed through the Lyapunov approach. The simulation results verify that the designed method could force the AUV to track the target until rendezvous, and the model uncertainty is addressed better via the composite learning algorithm.