Extreme Learning-Based Robust Adaptive Path Tracking Control of Underactuated Unmanned Surface Vessel

An extreme learning-based robust adaptive path tracking control scheme was proposed for underactuated unmanned surface vessels(USVs) with unknown dynamics, parameter uncertainties, and disturbances. Firstly, the surge-guided line-of-sight guidance law was used to guide the surge speed and heading angle at the same time, so as to avoid the singularity of the guidance process and make the USV converge to the desired path quickly. Secondly, the unknown dynamics including system uncertainties and external disturbances were encapsulated into a lumped unknown term, and hidden layer nodes were randomly generated by the single-hidden layer feedforward network(SLFN) of the extreme learning machine to identify the unknown term and avoid relying on USV prior knowledge and dimension explosion problem. Then, by designing an adaptive compensator for the approximation residual, the output weight and the approximation residual of the SLFN were updated online at the same time to form a dual-channel learning mechanism, which can not only enhance the approximation ability but also improve the tracking accuracy. Finally, an adaptive path tracking controller was designed so that the surge velocity and heading angle guidance errors of USVs can gradually converge to a small neighborhood near the origin. Simulation studies verify the effectiveness and superiority of the proposed scheme.